esa-sceva/satcom-chunk-collection huggingface dataset:
# Cellular-V2X Communications for Platooning: Design and Evaluation
## 4 Platooning in Cellular V2X
### Mapping to C-V2X
#### 4.3.2 PM-to-PM Step  
This step requires that one PM sends its CAM to the subsequent PM only, hence P2P V2V is the most suitable communication mode. Like P2 MP V2V communications, also P2P ones require that the MCS is selected by the eNodeB. Regarding the reception range, the same considerations done for P2 MP V2V communications are valid. Link adaptation for P2P links can be performed dynamically be the eNodeB, which can take advantage of CQI periodically reported by the PMs. This allows the eNodeB to allocate RBs using the MCS that suits better the considered V2V link. However, the accuracy of the MCS selection depends on the frequency of CQI reporting: even if the distance between PMs belonging to the same platoon is almost constant, the variations of the surrounding environment in such high-mobility scenario may cause the interference to vary even within the same subframe. On the other hand, frequent reporting (e.g., every millisecond) is hardly feasible due to the large signaling overhead.  
When a vehicle joins a platoon, in addition to the Platoon ID, it also receives information about the relative position within the platoon, i.e., the PM ID. PMs are identified through a sequential number. When a PM, \(i\), needs to send its CAM to its follower, \(i+1\), it specifies the Platoon ID and the PM identifier of its follower in the transmitted CAM. Only PM \(i+1\) belonging to the platoon Platoon ID will receive the CAM, whereas other PMs discard it.  
As far as transmission timing is concerned, the MAC layer of the PM has to initiate the handshake procedure with the eNodeB in order to obtain frequency resources. As mentioned before, the time between the resource request and the reception of the CAM is not lower than 15 ms. This meansthat up-to-date information can be propagated in the same cycle only if a delay of 15 ms per hop is tolerated. This is not the case for the proposed simultaneous transmission scheme, according to which all PMs can start the handshake procedure with the eNodeB soon after receiving the CAM from the PL. It will be then the eNodeB to decide which PMs could transmit, over which resources, _even at the same time_, possibly according to frequency reuse constraints. Moreover, P2P V2V communications can exploit a configurable number of H-ARQ retransmissions to improve reliability. This is possible if the receiving endpoint of the V2V link sends a MAC-level acknowledgement in response to a transmission. The way this can be achieved is outside the scope of this work, although we refer the interested reader to [34], which discusses H-ARQ-related issues in D2D communications. Anyway, a retransmission is scheduled by the eNodeB in case of a negative acknowledgement, eight TTI after the previous transmission. Obviously, H-ARQ retransmissions affect the time needed to complete a CAM cycle. Moreover, taking into account H-ARQ retransmissions at the application layer is not trivial if sequential transmissions are used.
# A Survey of Multi-Focus Image Fusion Methods
## 2 Fusion Methods and Analysis  
As shown in Figure 3, image fusion can be divided into pixel-level image fusion, feature-level image fusion, and decision-level image fusion according to the information representation layer [6]. For the image source classification, image fusion can be divided into remote sensing image fusion, medical image fusion, multi-focus image fusion, multi-exposure image fusion and infrared, and visible image fusion [7]. Referring to the fusion method, image fusion can be divided into spatial domain image fusion and transform domain image fusion [8].  
Figure 3: Image fusion classification.  
This paper proposes a new classification method, that is boundary segmentation method, dividing the current mainstream multi-focus fusion methods into four categories: transform domain method, boundary segmentation method, deep learning method, and combinatorial fusion method. Table 1 lists the classification method and the current mainstream algorithms.
## 2 Theoretical Background
### Multi-Task Learning  
Multi-task learning is a machine learning techniques in which \(n_{T}\) learning tasks are solved at the same time, using commonalities and differences across tasks. This approach may result in improved learning efficiency and prediction accuracy [12; 13; 14; 24], although the possibility of improvement depends on how information is encoded in the data. In this work, MTL will be applied, for the first time, to luminescence sensing, where the luminescence data are dependent on two quantities, oxygen concentrations and temperature, which are otherwise hard to extract separately.  
An example of a simple MTL network architecture, which reflects the architectures later used in the paper, is shown in Figure 1. This network consists of a series of common hidden layers, followed by two branches (\(n_{T}=2\)) each consisting of several task-specific hidden layers.  
The layers marked in Figure 1 as \"common hidden layers\" generate an output, that is typically called a \"shared representation\". The name comes from the fact that the output of those layers is used to evaluate both \(y_{1}\) and \(y_{2}\). The shared representation is then the input of a set of \"task-specific hidden layers\", that learn how to predict \(y_{1}\) and \(y_{2}\) better. Note how the common hidden layers are shared with both the tasks of predicting \(y_{1}\) and \(y_{2}\), while the task-specific hidden layers are specific to each task separately. The MTL network of Figure 1 uses the common hidden layers to find common features beneficial to each of the two tasks. During the training phase, learning to predict \(y_{1}\) will influence the common hidden layers and therefore, the prediction of \(y_{2}\), and vice-versa. A set of task-specific hidden layers will then learn specific features to each output and therefore improve the prediction accuracy. The implicit assumption here is that the tasks have something in common; otherwise this approach will not produce the desired result.  
Multiple cost functions \(L_{i}\) with \(i=1,...,n_{T}\), with \(n_{T}\) the number of tasks, are required to use this network architecture. In the training phase, a global cost function \(L\), defined as a linear combination of the task-specific cost functions with weights \(\alpha_{i}\) will be minimized  
\[L=\sum_{i=1}^{n_{T}}\alpha_{i}L_{i}. \tag{2}\]  
The parameters \(\alpha_{i}\) have to be determined during the hyper-parameter tuning phase to optimize the network predictions. In this paper, being the cost function the MSE (Equation (1)), the global cost function of Equation (2) is  
\[L=\sum_{i=1}^{n_{T}}\alpha_{i}\frac{1}{n}\sum_{j=1}^{n}\sum_{k=1}^{d}(y_{k}^{[ j]}-\hat{y}_{k}^{[j]})^{2} \tag{3}\]  
where \(n_{T}\) is the number of tasks; \(n\) is the number of observations in the input dataset; \(y^{[j]}\in\mathbb{R}^{d}\) is the measured value of the desired quantity for observation \(j\), with \(j=1,...,n\); \(\hat{y}^{[j]}\in\mathbb{R}^{d}\) is the output of the network, when evaluated on the \(j\)th observation.
# An Efficient Steganographic Protocol for WebP Files
## 2 Materials and Methods
### Background Information about WebP Format  
WebP is an image format developed by Google in order to save web resources. It supports both lossy and lossless compression of pictures. WebP files are designed to store still images or animations. Optionally, they may contain alpha channel, color profile, and metadata. The maximum dimensions of such images are 16,383 \(\times\) 16,383 px. One of the biggest strengths of WebP is its predictive coding drawn from the VP8 video format. This algorithm uses nearby values to predict pixel value in the block. Only the difference is saved; in this way, a high level of compression can be achieved. According to claims by Google, WebP images can be 26% smaller than PNG and 25-34% smaller than JPEG.  
The structure of WebP files consists of RIFF (Resource Interchange File Format) chunks. They are built of three sections: four bytes for the identifier, four bytes for the size, and the remaining bytes for data. The size concerns only the data part, which means that the whole chunk length is the data length + 8 bytes. The first chunk of a WebP file is a RIFF chunk that contains the 'WEBP' identifier and one of the possible chunks: 'VP8' for simple lossy format (the last character is 0x20--space), 'VP8X' for lossless compression without advanced functions, or 'VP8X' for extended format. The first 16 bytes of a lossless WebP file may look similar to Figure 1. As can be seen, the chunk size is 0x3 dc6 (in little median), so the file size is 15,814 + 8 = 15,822 bytes.  
'VP8' and 'VP8L' chunks contain VP8 bitstream data [64] and WebP Lossless Bitstream [65], respectively. More complex are 'VP8X' chunks, which consist of the following:  
* information about features used in the file;
* optional ICCP chunk with color profile;
* optional ANIM chunk with animation data;
* image data;
* optional EXIF chunk with Exif metadata;
* optional XMP chunk with XMP metadata;
* optional list of unknown chunks.  
These parts are present in the order indicated above. Optional unknown chunks are intended to be used in the future as the standard evolves and should be ignored by readers to achieve backwards compatibility.  
There are also some disadvantages of the WebP format. Although it is more than 10 years old, it still suffers from compatibility problems. In fact, these images are rarely used outside the web and the majority of users are clueless as to how to open downloaded pictures. The study conducted by the mozjpeg project regarding lossy encoding techniques revealed that WebP turned out to be better in some scoring algorithms, but comparative or worse according to others [66]. A serious threat was revealed in September 2023, when a buffer overflow was discovered [67] in both libwebp (CVE-2023-4863) and imageio (CVE-2023-41064). This vulnerability was acknowledged as critical and received the maximum severity score.
## 3 Results and Discussion
\begin{table}
\begin{tabular}{c c c c c c c} \hline
**Compound** & \multicolumn{2}{c}{**WWS**} & \multicolumn{2}{c}{**Uncooked P-WWS**} & \multicolumn{2}{c}{**Cooked P-WWS**} \\ \hline  & **FP** & **BP** & **FP** & **BP** & **FP** & **BP** \\ \hline _Phenelic acids_ & & & & & & \\ \hline Vanillic acid & 0.2 \(\pm\) 0.0 & 0.6 \(\pm\) 0.0 A & n.d. & 0.3 \(\pm\) 0.0 B & n.d. & 0.2 \(\pm\) 0.0 C \\ Springic acid & 2.2 \(\pm\) 0.4 a & 4.4 \(\pm\) 0.5 A & 0.7 \(\pm\) 0.0 b & 3.6 \(\pm\) 0.2 B & 0.4 \(\pm\) 0.0 c & 2.7 \(\pm\) 0.2 C \\ \(p\)-Hydroxybenzoic acid & n.d. & 3.4 \(\pm\) 0.3 A & n.d. & 1.2 \(\pm\) 0.3 B & n.d. & 0.3 \(\pm\) 0.0 C \\
4-Ferulylylamine acid & 1.2 \(\pm\) 0.1 a & n.d. & 1.1 \(\pm\) 0.1 a & n.d. & 0.9 \(\pm\) 0.0 b & n.d. \\ Vanillin & n.d. & 0.5 \(\pm\) 0.0 B & n.d. & 0.8 \(\pm\) 0.1 A & n.d. & 0.3 \(\pm\) 0.0 C \\ \(p\)-cumamic acid & 0.7 \(\pm\) 0.1 a & 2.2 \(\pm\) 0.1 A & 0.8 \(\pm\) 0.1 a & 0.6 \(\pm\) 0.0 B & 0.8 \(\pm\) 0.0 a & 0.7 \(\pm\) 0.0 B \\ Ferulic acid & 0.9 \(\pm\) 0.1 a & 7.4 \(\pm\) 0.3 A & 0.6 \(\pm\) 0.0 b & 2.81 \(\pm\) 1.6 B & 0.5 \(\pm\) 0.0 c & 14.2 \(\pm\) 0.8 C \\ Ferulic acid isomer 1 & n.d. & 34.1 \(\pm\) 2.0 A & n.d. & 32.3 \(\pm\) 1.9 A & n.d. & 22.5 \(\pm\) 1.4 B \\ Snapic acid & n.d. & 2.0 \(\pm\) 0.2 B & n.d. & 3.3 \(\pm\) 0.4 A & n.d. & 3.5 \(\pm\) 0.1 A \\ Diferulic acid & 0.6 \(\pm\) 0.0 b & n.d. & 0.6 \(\pm\) 0.0 b & n.d. & 0.8 \(\pm\) 0.1 a & n.d. \\ \hline _Cumarins (lactone)_ & & & & & & \\ \hline Coumarin & 0.3 \(\pm\) 0.0 c & 0.5 \(\pm\) 0.0 B & 0.8 \(\pm\) 0.1 a & 0.8 \(\pm\) 0.0 A & 0.5 \(\pm\) 0.0 b & 0.3 \(\pm\) 0.0 C \\ \hline _Flatones-Czyloside_ & & & & & & \\ \hline Apigenin-6-Carabinoside-8-C-hexoside isomer 1 & 1.6 \(\pm\) 0.1 a & n.d. & 1.2 \(\pm\) 0.1 b & n.d. & 0.9 \(\pm\) 0.0 c & n.d. \\ Apigenin-6-Carabinoside-8-C-hexoside isomer 2 & 1.1 \(\pm\) 0.0 a & n.d. & 1.1 \(\pm\) 0.1 a & n.d. & 0.7 \(\pm\) 0.0 b & n.d. \\ \hline _Flatones_ & & & & & & \\ \hline
3,4,5-trihydroxy-3,7-dimethylflavone & 9.6 \(\pm\) 0.8 a & 19.1 \(\pm\) 1.1 A & 6.4 \(\pm\) 0.5 b & 13.6 \(\pm\) 0.9 B & 7.5 \(\pm\) 0.4 b & 9.1 \(\pm\) 0.7 C \\ \hline Total (single fraction) & 18.4 \(\pm\) 1.6 a & 140.8 \(\pm\) 7.3 A & 13.3 \(\pm\) 1.0 b & 84.6 \(\pm\) 5.4 B & 13.0 \(\pm\) 0.5 b & 53.8 \(\pm\) 3.2 C \\ \hline Total (FP + BP) & 159.2 \(\pm\) 8.9 a & & 97.9 \(\pm\) 6.9 b & 66.8 \(\pm\) 3.7 c \\ \hline \end{tabular} Abbreviations. FP, free phenol; BP, bound phenol, n.d.: not detected. Data are expressed as means \(+\) standard deviation (or \(\pm\) 4). Different lowercase letters in the same row mean significant differences (\(p<0.05\)) among free phenolic compounds; different capital letters in the same row mean significant differences (\(p<0.05\)) among bound phenolic compounds.  
\end{table} 
Table 3: Free and bound phenolic compounds determined in whole-wheat semolina (WWS) and uncooked and cooked pasta made with 100% whole-wheat semolina (P-WWS), expressed in mg/kg dry weight (dw).
###### Abstract  
In view of the importance of accurately measuring the relative density of a selective laser melted (SLMed) part for optimizing the selective laser melting (SLM) processing parameters, suitable procedures of the Archimedes method considering the surface-connected cavities were proposed by comparing the results using the Archimedes method with image analysis. The effects of the SLM processing parameters on the relative density of AISi10 Mg were investigated using the proposed procedures of the Archimedes methods and image analysis. Fourteen SLMed samples were produced by different SLM processing parameters according to Doehlert Matrix. The regression models correlating relative density and three SLM processing parameters (laser power, scan speed, and hatching distance) were built and the optimum parameter combination to get a high relative density was obtained. By plotting the response surfaces and contours of the regression models, it was found that the relative densities are both higher at the combination of the higher scan speed, higher power, and lower hatching distance and at the combination of a lower scan speed, a moderate laser power, and a optional hatching distance. It was also found that the parameter of hatching distance is the crucial parameter to get a high relative density and to get high mechanical property.  
Architeredes method; image analysis; selective laser melting; optimization of parameters; AISi10 Mg alloy +
Footnote †: journal: Applied Sciences
# Mobile Robot for Security Applications in Remotely Operated Advanced Reactors
## 11 Discussions
The parameterization of robot performance is explored, highlighting the complexity in formulating a mathematical expression for quantitative assessment. The discussion touches upon industry-wide standards and the variation in measurement parameters based on the type of robot considered, such as industrial robots or mobile robots like unmanned vehicles, quadruped robots, and humanoid robots. Performance measurement criteria encompass self-awareness, human-awareness, system performance, autonomy, and human-robot interaction. Metrics for self-awareness, human-awareness, and system performance involve subjective and qualitative measures. Autonomy is measured through various techniques, including comparison competitions and neglect tolerance, while human-robot interaction is evaluated through metrics like trust perception, task effectiveness, and interaction effort [74; 75; 76; 77; 78]. The comprehensive parameterization and measurement of robot performance contribute to a deeper understanding of the capabilities required for successful deployment across various applications. These findings provide valuable insights for the development and evaluation of future robotic systems, ensuring their effectiveness, autonomy, and seamless interaction in diverse environments. Furthermore, the comparative analysis of dog robots, specifically Boston Dynamics' Spot [81], Ghost Robotics' Vision 60 [82], and ANYJotics' ANYmal [83] robots, reveals crucial insights into their operational, sensory, technical, dexterity, and software capabilities. This exploration is particularly relevant in the context of enhancing the physical security conditions of nuclear power plants (NPPs). The detailed comparison informs the selection of an optimal dog robot for future research on enhancing the physical security conditions in nuclear power plants. The choice depends on specific requirements and priorities. For applications demanding robust radiation monitoring, the SPOT robot's superior rad sensors make it a preferred choice. The Ghost Robotics robot, with its advanced computing power, communication flexibility, and simulation capabilities, presents itself as a versatile option for diverse research applications. The specific operational, sensory, technical, dexterity, and software capabilities of each robot should be aligned with the unique challenges and goals associated with securing nuclear facilities. Building upon the insights gained from the literature review on basic robot performance and the state-of-the-art comparison of popular dog robots, a new research direction is proposed. This roadmap outlines experiments, data collection methods, and key parameters for assessing the performance of mobile dog robots in security applications, specifically in the context of remotely operated advanced reactors.
## 2 Experimental Procedure  
### Co\({}_{2}\)SnO\({}_{4}\) Film Deposition  
Co\({}_{2}\)SnO\({}_{4}\) (CTO) films were obtained using 0.02 mole of cobalt (II) chloride dihexahydrate (CoCl\({}_{2}\).6H\({}_{2}\)O) mixed with 0.01 M of chloride dihydrate (SnCl\({}_{2}\).2H\({}_{2}\)O) in 100 mL of distilled water at ambient temperature. All the used reagents were commercially available, and no purification was needed. Then, 2.0 M of NaOH solution, used as mineralizer, was added under magnetic stirring for 15 min; a blue precipitate was obtained instantaneously. The final molar ratio of [Sn]:[Co] was fixed at 1:2. The mixture was then transferred to Teflon-lined stainless steel autoclave and kept at 240 \({}^{\circ}\)C for 48 h. The resulting precipitates were collected by centrifugation and washed with distilled water, and finally dried in an electric oven at 120 \({}^{\circ}\)C for 12 h. The obtained Co\({}_{2}\)SnO\({}_{4}\) precursor was dissolved in absolute alcohol and sprayed on glass substrates at a growth temperature of 500 \({}^{\circ}\)C.  
During the spray deposition, nitrogen (N\({}_{2}\)) was used as a carrier gas. In addition, to ensure the best uniformity of the deposited layers, the horizontal sweep of the nozzle (0.5 mm diameter) was optimized. The flow rate of the precursor mixture was taken constantly at 5 mL.min\({}^{-1}\). After deposition, the elaborated films were allowed to cool to room temperature.  
Finally, two Co\({}_{2}\)SnO\({}_{4}\) samples were prepared. One of them (denoted as CTO\({}_{\rm ann}\)) was calcined at 800 \({}^{\circ}\)C for 8 h with a heating rate of 10 \({}^{\circ}\)C/min under standard atmospheric conditions, after which the sample was allowed to cool to room temperature. The aim of this step was the investigation of the annealing effects on the Co\({}_{2}\)SnO\({}_{4}\) layer's sensitivity toward ethanol vapor, which is probably controlled by the phase transition in the as-prepared layer. The choice of temperature and calcination duration were in order to ensure the synthesis of a stable sensitive phase, as the working temperatures under the ethanol vapor would be varied. The second sample, which remained as prepared, was denoted as CTO\({}_{\rm as}\). The morphological results are reported in Figure 1.  
We noted that the surface morphologies of CTO\({}_{\rm ann}\) (Figure 1b) were more granular and porous compared with the as-prepared sample (CTO\({}_{\rm as}\)) presented in Figure 1a. These morphological differences could be beneficial for ethanol detection by CTO\({}_{\rm ann}\).
## 2 Materials and Methods
### Evaluation of Texture and Sensory Properties of Cooked Noodles  
Texture analysis is the main evaluation method for noodles [30]. Texture analyzer can measure the hardness, cohesiveness, springiness, gumminess, chewiness, and adhesiveness of the sample in the measurement mode. The physical properties, viz., tension, hardness, cohesion, elasticity, chewiness, stickiness and adhesiveness of noodles were assessed using a texture analyzer (Brookfield CT3 Texture Analyzer, Brook-field Engineering Laboratories, Middleboro, MA, USA). Firstly, the noodles were boiled in boiling water for 4 min. Subsequently, the noodle strands of final length of 5 cm were taken out and fixed on a TA-RT-KIT fixture. The tension, hardness, cohesion, elasticity, chewiness, stickiness and adhesivenessof the noodles were measured using a hook-shaped TA-KF probe. The parameters set for texture profile analysis (TPA) were as follows: test speed of 0.5 mm/s; distance of 1.33 mm; load of 0.067 N; adjustment measurement of 14 mm. The physical characteristic results were derived from the texture profile and force-time curve produced by the instrument software and expressed digitally. Three individual noodles were taken from each group for testing.  
The control and 20% SPI noodles were also subjected to IDDSI (International Dysphagia Diet Standardisation Initiative) testing in order to categorize the texture of the noodles for the aged population [31]. The noodles were subjected to a fork pressure test, fork drip test and a spoon tilt test as per Level 5 food testing of IDDSI since the noodles showed the characteristics of Level 5 food such as the possibility of being eaten with a fork or spoon, scooped and shaped and being soft and moist with no separate thin liquid. For the fork pressure test, the thumb finger was used to apply pressure to the noodle samples using a fork until the thumbnail blanched. The pressure applied was \(\sim\)17 kPa which is equivalent to the pressure of the tongue while swallowing. The fork drip test was carried out by picking the noodles with a fork to examine the shape of the noodles on the fork and the flow behavior through the fork prong. The spoon tilt test was carried out by scooping the noodles with a spoon and slowly tilting the spoon sideways to evaluate the adhesiveness and cohesiveness of the samples.  
Sensory analysis of the cooked noodles was performed by 30 panelists (10 males and 20 females) who were university students/employees at the Department of Seafood Science aged between 20 and 40 years. The cooked noodles were put into uniform plastic cups and covered; then, they were presented following a design balanced for presentation order (William Latin square) to the panelists. The noodles were evaluated for flavor, taste and overall acceptability. A nine-point liking scoring method was used to measure the acceptability of the noodles, where 1 represents \"extremely dislike\" and 9 represents \"extremely like\".