esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Methodology
### Battery Lifespan

Table 6: Maximum number of packets according to duty cycle. achieve a 1-year lifetime if sending a message every 5 min and a theoretical lifetime of 6 years for infrequent communication. Moreover, an energy consumption of 0.05-0.44 mJ and a battery lifetime between one and 13 years, respectively, are obtained for a device running on two AA batteries when transferring data from 1 to 10 times per hour [53]. For the chosen packet payload sizes, the calculator [79] is applied to estimate the differences in energy consumption for several types of batteries which can supply sensor nodes, as presented in Table 7. The assumptions considered are processing power of 15 mW for 5 ms, confirmed reading of a sensor value per each period, and sleep mode consumption of 10 \(\mu\)W. The worst-case results have been calculated for the usage of SF12, transmission power of +14 dBm, and with the acknowledgment in the second Rx window. The best-case results have been calculated for the usage of SF7, transmission power of +2 dBm, and with the acknowledgment in the first Rx window. As can be seen from the comparison of the collected results, a substantial energy consumption reduction can be achieved by transmitting smaller data packets. Since sensor nodes are supplied with cell batteries upon delivery, purchasing a solar panel initially represents an additional cost in contrast to the sensor nodes, which are only battery-powered. The cost of a set of cell batteries represents approximately 12 times the cost of a cheaper sensor or approximately 2 times the cost of a more expensive sensor [80]. Additionally, the cost of a solar panel represents 8 times the cost of a cheaper sensor, or 4/3 times the cost of a more expensive sensor. For instance, while temperature and luminosity sensors are cheaper, humidity and atmospheric pressure sensors are examples of more expensive sensors. In the case when the duration of the battery's lifetime is short and frequent battery changes are required, the application of solar power supply represents an effective solution in the long term. However, due to the limited capacity and lifetime of batteries, they need to be replaced after a particular time, even if solar power is used. However, in sensor networks where more sensor nodes are implemented, the energy consumption per sensor node for the exact total generated network traffic in the same period becomes lower than when fewer sensor nodes are implemented. Thereby, the battery life per each sensor node is extended, and the battery needs to be changed less often, which demands less energy from the solar collector. In Table 7 data related to battery consumption are presented in cases where different packet sizes, different numbers of messages, and different network parameters, such as SF, are used. As presented, the longest battery life is achieved when smaller packets are sent, when packets are sent less often, and when SF7 is used, and not SF12.
# On the Impact of Multiple Access Interference in LTE-V2X and NR-V2X Sidelink Communications +
## 3 Cellular-V2X Technologies  
In this section, we review the key technological aspects related to the evolution of V2X systems towards 5G systems [31], focusing on the transmission modes for sidelink operations in LTE-V2X and NR-V2X.  
Figure 1: Considered vehicle platooning scenario.
## 2 Background Studies
### Scoring-Based Mortality Prediction  
Predictive scoring systems are disease severity measurements that are utilized to predict outcomes of a patient's condition, most commonly mortality, in the ICU. Some common scoring systems for mortality forecasting include the Acute Physiology and Chronic Health Evaluation (APACHE) [29], the Sequential Organ Failure Assessment Score (SOFA) [30], and the Simplified Acute Physiology Score (SAPS) [31]. [PERSON] et al. [32] developed a computational model that can predict mortality using a set of SOFA scores. When compared to previous years' work, they discovered that an ensemble model of linear and LR yields a higher AUROC. [PERSON] et al. [33] assessed the performance of the Acute Physiology and Chronic Health Evaluation (APACHE)-III, APACHE-IV, the Sequential Organ Failure Assessment (SOFA), and the Oxford Acute Severity of Illness Score (OASIS). In the CICU group, the assessed risk scores revealed uneven performance for mortality risk categorization across admission diagnoses. Traditional scoring methods, on the other hand, do not fully utilize a vast quantity of patient data and can only forecast with a limited degree of accuracy.
# Research on a Support System for Automatic Ship Navigation in Fairway
## 4 Automatic Navigation System of a ship in a Fairway  
### The Concept of this System  
In this research, the automatic ship navigation system is proposed to perform three tasks. First, the ship's rudder is automatically controlled to keep it tracking the planned route. To carry out this task,  
\begin{table}
\begin{tabular}{c c c c} \hline
**Coefficient** & **Value** & **Coefficient** & **Value** \\ \hline \(X_{\beta r}^{\prime}\) & \(-0.5012\) & \(Y_{\beta r}^{\prime}\) & \(-0.3561\) \\ \(X_{\mu u}^{\prime}\) & \(0.183\) & \(N_{\beta}^{\prime\prime}\) & \(0.1379\) \\ \(Y_{\beta}^{\prime\prime}\) & \(0.2496\) & \(N_{\beta r}^{\prime}\) & \(-0.0488\) \\ \(Y_{\tau}^{\prime}\) & \(0.0542\) & \(N_{\beta\beta}^{\prime\prime}\) & \(-0.0782\) \\ \(Y_{\beta\beta}^{\prime}\) & \(0.8755\) & \(N_{\tau r}^{\prime\prime}\) & \(-0.0430\) \\ \(Y_{\tau}^{\prime\prime}\) & \(-0.0028\) & \(N_{\beta\beta r}^{\prime\prime}\) & \(-0.0480\) \\ \(Y_{\beta\beta r}^{\prime}\) &the fuzzy logic controller is fed with two inputs and one output. The structure of the track-keeping system proposed in this research is the same as that of others in previous studies.  
Second, ship speed is automatically controlled by a PID controller, and is decreased when the ship approaches the berthing area. The desired speed of the ship in each track is predetermined to ensure that the ship approaches the berthing area with the desired speed. PID algorithms will calculate the input signal to control propeller RPM.  
Finally, to confirm the feasibility of such an automatic ship berthing system, the ship heading line needs to be adjusted to the appropriate direction concurrently with the adjustment of ship speed. Because the ship's rudder is ineffective at low speed, the bow thruster is used for directional control instead of the ship's rudder. The fuzzy logic is also designed with this in mind.  
The system includes the following components: Ship Route Generator, Navigation Algorithms, a Decision-Making Block, and the Control System. The aim of these components is presented in the following section. The architecture of the automatic ship navigation system is shown in Figure 3.
# Review on Structural Health Evaluation with Acoustic Emission
## 2 Signal Attenuation  
An important issue during any structural inspection using AE methods is the decrease in signal intensity or attenuation. On the AE side, signal-to-noise ratio is important in signal detection. In SHMapplications, quantitative system modeling is often desired and attenuation behavior needs to be a part of successful SHM system design [16]. The same phenomena at lower frequencies are referred to as structural damping and a recent review relative to composite materials [17] provides useful backgrounds. This topic is also indirectly related to the mechanical properties of polymers since most polymers exhibit viscoelastic behavior and strong acoustic damping [18; 19].
# Advances in Genome Sequencing and Natural Rubber Biosynthesis in Rubber-Producing Plants
TKS belongs to the family Asteraceae, subfamily Cichorioideae, and is extensively distributed in temperate zones [14]. It accumulates NR in the laticifer cells of its roots, with a dry rubber content of up to 20% [15]. Moreover, TKS exhibits strong environmental adaptability, a short life cycle, and a mature transgenic system, positioning it as a model plant for rubber production and research. However, self-incompatibility and a high degree of heterozygosity pose obstacles to its domestication [8,15]. Lettuce, belonging to the Asteraceae family, is a temperate annual or biennial herb with abundant latex. It is an easily cultivated, self-pollinating annual plant with a 4-5 month life cycle, which makes it an ideal candidate for research in rubber production [16]. The relatively low NR content in latex is the primary limiting factor for the development and utilization of lettuce as a rubber-producing plant. Guayule is a perennial shrub that grows 0.3-0.9 m tall, inhabits arid and semi-arid areas, and can survive in desert environments with temperatures exceeding 40 \({}^{\circ}\)C and severe aridity [1,17]. NR derived from guayule bark parenchymal cells mainly consists of cis-1.4-polyisoprene with a molecular weight of 1280 kD, comparable to that of _H. brasiliensis_. Additionally, guayule rubber lacks allergenic proteins, making it suitable for specialized use in the medical field [18]. In a 2-year-old guayule plant, NR is present in the bark, stem, root, and leaf, with an average content of 8% of the dry weight [19]. However, guayule NR is consistently contaminated with resin, which necessitates its removal through solvent extraction to enhance the properties of the NR [8]. Over the past decade, significant progress has been made in the study of rubber-producing plants. Here, we reviewed the progress in the field of genome sequencing, the biosynthesis pathways of natural rubber, the components and regulated mechanisms of the rubber transferase complex, as well as future research directions for rubber-producing plants.
### Background  
#### 1.1.1 Entropy  
Suppose a random variable \(X\) takes on values \(x_{1},\ x_{2},\cdots,x_{n}\) with respective probabilities \(p(x_{1}),\ p(x_{2}),\cdots,p(x_{n})\). The surprise evoked upon learning that \(X=x_{i}\) is defined as \(-\log_{2}p(x_{i})\). The entropy of the random variable \(X\), denoted by \(H(X)\), is determined to be the expected (average) amount of surprise evoked by learning all values of \(X\), which is given by  
\[H(X)=E(-\log_{2}(X))=-\sum_{i=1}^{n}p(x_{i})\log_{2}p(x_{i}).\]  
The entropy \(H(X)\) can also be interpreted as the amount of uncertainty that exists in the random variable \(X\) or the average amount of information received when all the values of \(X\) are observed.
# Vehicle-in-the-Loop in Global Coordinates for Advanced Driver Assistance System
## 2 Vehicle in the Loop
### Sensor Emulation
This information can be generally defined by the distance \(A^{L}\), incidence angle \(B^{L}\), the curvature of the lane \(C^{L}\), and the rate of curvature changing \(D^{L}\). The distances between the line and the ego vehicle and the incidence angle can be calculated using (17) and (18). The position \(\left\{x_{FW}^{EGO},y_{FW}^{EGO}\right\}\) is generally defined as the center axis of the front wheel in the ego vehicle to obtain the relative distance. \[A^{L}=\sqrt{\left(x^{L}-x_{FW}^{EGO}\right)^{2}+\left(y^{L}-y_{FW}^{EGO} \right)^{2}} \tag{17}\]  
\[B^{L}=\psi^{EGO}-\psi^{L} \tag{18}\]  
In addition, the curvature and curvature derivatives, which are line-specific information, can directly use the information defined in Table 3. These can be derived in advance through polynomial curve fitting, as shown in Figure 4b. In the real world, the road line information is calculated with a certain section of the line that is ahead of the ego vehicle and there are environmental impact areas such as shadows, light, occlusions and standing water, as shown in Figure 5[35; 36]. When some of the interesting line points exist in the area, the perception rate of the vision sensor can be reduced. To extract the affected line points, some algorithms, called the point in polygon (PIP), are given in the previous studies. Two of the well-known ones are the ray casting algorithm and the winding number algorithm [37; 38; 39; 40]. A set of perceptible \(P_{out}\) that exists outside is extracted from the interesting points using the algorithm in (19). \[P_{out}=PIP_{out}(P,A) \tag{19}\]  
Figure 4: (**a**) Calculation of radar detected object information; (**b**) Calculation of road line information by the vision sensor. \(P\) and \(A\) represent each set of interesting line points and area node points. Detail of this algorithm is discussed in Appendix A. The derivable conditions for the road line information can be substituted with experimental values. The recognition and judgment information generated by the radar and vision sensor emulation module can replace the real sensor for objects that do not actually exist.
## 3 Results and Discussion
### Operator, Company Name and Installation Modules  
The term \"installation\" implies a stationary technical unit where one or more activities are carried out at the same site that could have a negative environmental impact. Authorization is generally granted to the operator of an installation. The operator is the natural or legal person who is the owner or the manager of the installation and has the authority and ability to ensure compliance with the authorization [8]. He/she has to comply with regulations, directives and licenses, and to ensure that all necessary measures are taken to protect the environment [37].  
Therefore, the _installation module_ represents the centerpiece of the database, which links together all the other modules.  
Data on the operator, company name and installation are available in the web platform, including address, coordinates, activities, tax code, Value Added Tax (VAT) identification number, date and place of birth, telephone number and mail address. Users can consult the above-mentioned data and view the spatial-distribution of the installations on the territory in the GIS map.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Stakeholders Involved** & **Period** & & **Results** \\ \hline Staff (Officials of the Province of Ascoli Piceno) & February 2020 & (1) & User needs and territorial features were clarified (2) & \\  & & (2) & the first data (both digital data and paper archive) were collected & \\  & & & All participants (10): & \\  & & (1) & believed that the environmental data are not currently managed & \\  & & & and shared in a reasonable way & \\ Polices forces and competent authorities & May 2020 & (2) & appreciated the usefulness of the tool for sharing environmental & \\  & & & (3) & agreed to participate in the testing phase \\  & & & (4) & provided suggestions \\  & & & All participants (8): & \\  & & & (1) & believed that environmental data are not currently managed and \\ Operators & February 2021 & (2) & appreciated the usefulness of the tool for sharing environmental & \\  & & & (3) & agreed to participate in the testing phase \\  & & & (4) & provided suggestions \\ \hline \hline \end{tabular}
\end{table}
Table 1: Results of the preliminary meetings.
## 3 Methodology
### AG Module  
For the building extraction task from high-resolution remote sensing images, the attention gate (AG), as a spatial attention mechanism, enables the model to selectively focus on different spatial regions of the image to accurately extract building contours in remote sensing images, which can significantly improve segmentation model performance [40]. AG contains two branches for different feature processing. These two branches are fused at the pixel level, which can better integrate the multidimensional information of the input image. AG uses two different activation functions: ReLU for feature normalization and Sigmoid for generating attention coefficients. The combination of these two activation functions introduces nonlinearity to AG. Second, AG contains three sets of trainable parameters that control the gate coefficients W\({}_{\beta}\), attention coefficients W\({}_{\chi}\), and output adjustment parameters, making AG more adaptable to specific segmentation tasks. Additionally, its behavior can be controlled by adjusting the parameters. AG is an efficient, controllable and scalable spatial attention mechanism that is well-suited for fine-grained building segmentation tasks from high-resolution remote sensing images. It improves the model's ability to identify key target areas by dual-branch feature fusion, adjustable parameters, and suppressing irrelevant regions. Integrating AG into standard convolutional networks can significantly improve the extraction accuracy of small and densely distributed buildings [41]. The AG module structure is illustrated in Figure 4.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Block** & **Type** & **Filter** & **Channel Size** & **Output Size** \\ \hline Block 1–4 & Conv1 & (3,3) & 64 & 256 \(\times\) 256 \\  & Maxpool1 & (2,2) & 64 & 128 \(\times\) 128 \\  & Conv2 & (3,3) & 128 & 128 \(\times\) 128 \\  & Maxpool2 & (2,2) & 128 & 64 \(\times\) 64 \\  & Conv3 & (3,3) & 256 & 64 \(\times\) 64 \\  & Maxpool3 & (2,2) & 256 & 32 \(\times\) 32 \\  & Conv4 & (3,3) & 512 & 32 \(\times\) 32 \\  & Maxpool4 & (2,2) & 512 & 16 \(\times\) 16 \\ Block 5 & PPM & & 1024 & 16 \(\times\) 16 \\ Block 6–9 & Up\_conv4 & Up–(3,3) & 512 & 32 \(\times\) 32 \\  & AG4 & & 512 & 32 \(\times\) 32 \\  & Up4 & & 512 & 32 \(\times\) 32 \\  & Up\_conv3 & Up-(3,3) & 256 & 64 \(\times\) 64 \\
# Evaluation of Operator and Patient Doses after Irradiation with Handheld X-ray Devices
## 4 Discussion
In addition, they reported that the concept of \"controlled area\" is smaller than the currently accepted version. [PERSON] et al. [25] focused on assessing the radiation doses received by the fingers and lens when employing various handheld X-ray devices. Their data underscored the efficacy of protective shields in diminishing finger dose exposure and emphasized that increased distance correspondingly reduced the lens dose. Similarly, [PERSON] et al. [26] evaluated the radiation doses received in specific regions--fingers, hands, interpupillary distance, sternum, and genital area--by radiology personnel during the operation of a handheld X-ray device. Their findings indicated that the dose exposure fell within the thresholds recommended by the ICRP [0.16 (0.12-0.23) per hour or 1.28 (0.96-1.84) per day]. These results align with the outcomes of the current research. However, a distinct methodological difference existed: instead of employing a mannequin, as in the current study, [PERSON] et al. [26] conducted their irradiations with personnel equipped with lead shields. It is pertinent to note that a limitation of the present research was the consistent distance maintained between the mannequin and the device. In practical settings, operatorsmight employ varied positions during X-ray operations, potentially altering the radiation dose to different organs. Future research endeavors could incorporate this variability by conducting evaluations across a range of positions.
# Improving Foraminifera Classification Using Convolutional Neural Networks with Ensemble Learning
## 2 Materials and Methods
### Training  
Once the augmented dataset is built, networks are trained using a pre-trained ResNet50, which, as the name suggests, is a 50-layer CNN. The input layer is a 224 \(\times\) 224 \(\times\) 3 zero-pad layer. There are 48 hidden layers, two of which are a max and an average pool, respectively. Networks of the ResNet family utilize residual blocks to maximize depth while diminishing the number of parameters [8]. We also used ResNet18, which works much the same but with 18 layers, GoogLeNet [27], which is 22 layers deep, and MobileNetv2 [28], which is composed of 20 layers. All networks used take 224 \(\times\) 224 \(\times\) 3 images as input, so the inputs are resized as such using Matlab's standard interpolation. This almost always changes the pictures' original resolution, making them smaller, so we can reasonably assume that some detail is lost in the process. Adapting the networks to take variable input resolution might be a possible future development for this approach.  
All networks were pre-trained with the open-source image dataset ImageNet, which contains millions of labelled images. Hyperparameters for all networks were set as follows:  
* Mini Batch Size: 30;
* Max Epochs: 20;
* Learning Rate: \(10^{-3}\).  
The main benefit of a pre-trained network is transfer learning. A modern neural network can carry over some of the knowledge gained in previous training cycles on a particular dataset when retraining it on a different dataset [8]. This carry-over of information is especially pronounced in deep learning models, because they operate on a wide array of weights and features. Transfer learning significantly reduces the time and number of images needed to train the networks, an advantage when working with relatively small datasets. All layers are tuned, none frozen.  
The output layer of the pretrained networks has to be adapted to accommodate the task of foraminifera classification. Thus, all the output layers were replaced with fully connected SoftMax layers of seven neurons, one for each class of foraminifera available in the dataset. Images of the dataset were also resized to match the dimensions of the input layers.  
By replacing the last layer with a seven-neuron SoftMax layer, the outputs form a vector \(\mathbf{v}\), whose values (scores) are:  
\[\left\{\begin{array}{l}0\leq\mathbf{v}_{i}\leq 1\ \forall\ i\\ \sum\limits_{i=1}^{7}\mathbf{v}_{i}=1\end{array}\right.\]  
By diversifying the models, the information processed by each is different. Score fusion is a method that combines the confidence values of each model to build a more robust prediction. The fusion technique used here is the sum rule [19], defined as:  
\[sum=\sum\limits_{i=1}^{N}\mathbf{v}\ ;\ out=argmax\ \left\{sum_{j}\right\},\ j=1 \ldots n\]  
where \(N\) is the number of models and \(n\) the size of each confidence vector \(\mathbf{v}\). The sum rule is one of the best fusion methods because it does not suffer from potentially destructive operations like multiplication by zero.