esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussions
### Angle Dependence and Dose Response  
The color intensities of red and green components measured with Nix being rotated with 15\({}^{\circ}\) intervals are shown in Figure 4 for four dose levels (0, 4, 10 and 40 Gy) of 160 kV X-rays. In the calculation of the color intensity (\(\Delta I\)) at each angle, the intensity of the unirradiated film (\(I(0)\) in Equation (2)) was given as that measured at 0\({}^{\circ}\). As seen in the figure, systematic angle dependence was clearly observed at all dose levels in both color components. The absolute variation ranges were similar in every case regardless of dose levels, which indicates this angle dependence is attributable to the anisotropic light scattering on the surface of the Gafchromic film. Accordingly, the relative errors became higher at lower doses according to the reduced color intensities. The angle dependence was seen in unirradiated (pre-irradiation) samples also. These findings indicate that the so-called 'orientation effects' or 'lateral response artifacts' reported for the Gafchromic EBT films measured with flatbed scanners [1,21,25,36,41,42,43,44, 25] also appear in measurements with a portable colorimeter.  
To confirm the effects of the angle dependence on dose assessments, the dose responses of red and green components were compared among the data obtained at four different angles (0\({}^{\circ}\), 90\({}^{\circ}\), 180\({}^{\circ}\) and 270\({}^{\circ}\)) as shown in Figure 5. From these plots, it was considered that some errors up to several percent could be potentially caused by the uncontrolled orientation of Nix in the measurement of EBT-XD. It is expected, however, that this error attributed to the angle dependence can be averted by the careful handling of Nix paying and attention to the film orientation during reading. More concretely, a user can obtain the color intensity for a dosimetry purpose by keeping the same orientation of the Gafchromic film when reading it before and after irradiation.  
Figure 4: Angle dependences of the color intensities as to the red and green components of EBT-XD measured with Nix with 15\({}^{\circ}\) intervals after irradiation with 160 kV X-rays at 0 (control), 4, 10 and 40 Gy.
# Compliance of Distribution System Reactive Flows with Transmission System Requirements
## 3 Case Study
### Results, Full Day
In contrast, the QV droop uses significantly more reactive power support, leading in occasion to a violation of the import limits, instead (e.g., see hour 15 in Figure 12). More specifically, with the proposed method, the total reactive power support from DER is 49.56 kVARh, while under the benchmark case it is 123.33 kVARh. This is a reduction of 60%. Figure 12: Reactive power exchange with the transmission system for the entire day when a QV droop curve support scheme is used (benchmark case). Figure 13: Reactive power exchange with the transmission system for the entire day when the proposed method is used.
# Product Demand Prediction with Spatial Graph Neural Networks
## 4 Experiments
### Experiment Setup  
#### 4.1.1 Datasets  
Overall Introduction  
In the experiments, we utilize the Avito Demand Prediction Challenge hosted on Kaggle as the benchmark, which presents participants with a unique opportunity to apply machine learning techniques to predict demand for an extensive range of products listed on Avito's platform. Avito, being Russia's largest classified ads service, encompasses a wide variety of categories, including electronics, real estate, and services, making it a rich source of data for such predictive modeling tasks. The objective of the challenge is to accurately predict the probability of an ad leading to a product transaction, based on the information provided in the ad's description, context, and metadata. This task has profound implications for both sellers by optimizing their ad placements for higher sales, and buyers, by enhancing their shopping experience through the prioritization of listings likely to meet their purchase intent.  
Detailed Data Information  
The dataset provided by Avito's team contains multiple modalities of information, including images, text, categorical, and continuous features. We provide their details as follows:  
* item id: Id of a particular advertisement.
* user id: Id of a user.
* region: The region that Ads belong to.
* city: The city that a Ad belongs to.
* top-level category: The top-level ad category as classified by Avito's ad model.
* fine-grain category: The fine-grain ad category as classified by Avito's ad model.
* param 1: The first optional parameter from Avito's ad model.
* param 2: The second optional parameter from Avito's ad model.
* param 3: The third optional parameter from Avito's ad model.
* title: The textual title for the Ad.
* description: The multi-sentence textual description for the Ad.
* price: The numerical value for the Ad's price.
* item seq number: Ad sequential number for the user.
* activation date: The date that the Ad was placed onto the platform.
* user type: The type of the user, including Private, Company, and Shop.
* image: The Id code corresponding to the image which is tied to a jpg file in train jpg. Considering that not every Ad has an image, we don't employ this feature for further analysis.
* image top 1: Avito's classification code for the image.
* deal probability: The target variable. This is the likelihood that the ad will actually sell the item. It is not possible to verify every transaction with certainty, so the value of this column can be any floating point number from zero to one.  
This dataset contains \(1,503,424\) records, which are randomly divided into train/validation/test sets according to the 70%/10%/20% ratio.
## 4 Optimization Method
### Improved Scheduling Strategy
#### 4.1.1 Traditional \"Following Electric Load\" (FEL) Strategy  
The FEL strategy assures that the electrical energy generated by the CCHP system matches the electric load demand at any time. Priority is given to the photovoltaic power generation to meet the demand of the electric load and electric chiller [33; 34; 35]. If photovoltaic power generation cannot satisfy the demand of the electric load and electric chiller, the gas turbine will be taken as a backup power to fill the demand of the power load. On the contrary, if the generated power from the photovoltaic power generation is greater than the power demand, the excess power is sold to the grid [34].
## 4 Si Transporters  
[PERSON] et al. [41] reported that silicic acid is co-transported by aquporins. Aquaporins belong to the major intrinsic protein family and help transport various types of substrates, such as Si, boron, urea, carbon dioxide and hydrogen peroxide [42]. Plant aquporins are categorized into seven subfamilies. Their specificity is governed by four amino acid residues (aromatic/arginine selectivity filters) located at specific positions. [PERSON] et al. [43] indicated that the Si transporters Lsi1 and Lsi6 belong to the nod26-like major intrinsic protein (NIP2) subfamily. Moreover, they are all influx transporters of Si. The aromatic/arginine (ar/arg) region and proton repulsion are major factors in the selectivetransport of molecules, and they play key roles as primary filters of the pores. Plant silicon transporters, such as OsLsi1, AtLsi1 and ZmLsi1, absorb and accumulate Si in different parts of plants (Table 1).
# IoT and UAV Integration in 5G Hybrid Terrestrial-Satellite Networks
## 3 UAV and IoT
### UAV and IoT: State-of-the-Art  
Studies have been carried out to show how UAVs can be integrated into the IoT world. The mentioned face recognition sensor crowd surveillance application [30] exploits a platform composed of UAVs and IoT devices. UAVs equipped with video cameras are connected to the ground station by using an LTE cellular network. When a security guard, for example, noticessuspicious behavior, he/she commands the UAV to take a video of the involved people and applies face recognition to verify if someone has a criminal record. The work in [31] found an optimal flying path for UAVs equipped with IoT sensors by using a location-aware multi-layer information map and utility functions based on sensor density, flight time, energy, and risk. Genetic algorithms were used to maximize these utility functions. An evacuation support system based on IoT and UAVs was proposed in [32]: the system, consisting of IoT devices, is controlled by an intelligent agent (agent-based IoT) and autonomously determines a suitable plan to support a quick evacuation. The work in [33] created an Intelligent Home Security System (IHSS), which has the ability to monitor sensors and control actuators such as UAVs. Experimental results showed the effectiveness and usefulness of this IHSS system. IoT and UAVs are promising in the agriculture field: IoT devices would help monitor and control crop parameters and increase the quality and quantity of food. For that purpose, the work in [34] distributed heterogeneous IoT devices in a crop field to monitor and record environmental parameters.
# Improved Ship Detection Algorithm from Satellite Images Using YOLOv7 and Graph Neural Network
## 2 Related Work and State of the Art  
### Ship Detection Data Collections Platform  
According to [PERSON] et al. [14], optical, infrared, and radar sensors are the most frequently utilized sensors in sea surveillance applications. Since the 1990s, radar has been a common technology for ship surveillance and detection. One of the most common types of ship detection that can be performed is by using a satellite-based sensor. This method involves collecting data from various sources.
# Hybrid Genetic and Spotted Hyena Optimizer for Flow Shop Scheduling Problem
## 1 Introduction
It operates on a population of candidate solutions and evolves them over generations using genetic operators, such as selection, crossover and mutation. GAs have been successfully applied to solve numerous combinatorial optimization problems, including the FSSP. In our hybrid approach, we combine the strengths of both SHO and GA to improve the overall search efficiency and solution quality. The proposed algorithm starts with an initial population generated by the SHO. During the search process, the SHO mechanisms are employed to explore the solution space and to update the hyenas' positions. After a predefined number of iterations, the GA is integrated into the algorithm to further refine the solutions. The GA takes the current hyena positions as an input population and performs selection, crossover and mutation operations to generate offspring. The offspring then replace some of the least fit hyenas in the population, ensuring the best solutions are retained. This hybridization of SHO and GA capitalizes on the exploration capabilities of the SHO and the exploitation abilities of the GA, resulting in a more robust and efficient algorithm for solving the FSSP. The proposed hybrid method is tested on a set of benchmark instances from the literature, and the results demonstrate its effectiveness in finding near-optimal solutions with competitive computational times. The successful application of the hybrid spotted hyena and genetic algorithm to the FSSP indicates its potential for addressing other complex combinatorial optimization problems in various domains. The main contributions of this paper can be summarized as follows:  
* Development of a Hybrid Algorithm: We propose a new hybrid algorithm that effectively integrates the exploration capabilities of SHO and the exploitation capabilities of GA. This combination ensures a more balanced and efficient search process, allowing the algorithm to find near-optimal solutions for the FSSP. * Adaptation of SHO and GA to FSSP: We adapt the SHO and GA methods to the specific requirements of FSSP, including encoding schemes, objective function evaluation and constraint handling. This adaptation ensures that the hybrid algorithm is well-suited to address the challenges posed by the FSSP. * Comprehensive Performance Evaluation: We perform a thorough performance evaluation of the proposed hybrid algorithm using a set of benchmark instances from the literature. The results are compared with those obtained by state-of-the-art algorithms, demonstrating the effectiveness of our hybrid approach in terms of solution quality and computational efficiency. * Hybrid Algorithm Behavior Analysis: We provide an in-depth analysis of the behavior of the hybrid algorithm, discussing the roles of the SHO and GA components and their interactions during the search process. This analysis helps to understand the factors contributing to the success of the algorithm in solving the FSSP and offers insights into potential improvements or adaptations to other combinatorial optimization problems.
## 1 Overview of BSNs
### BSN Requirements
For life-critical applications that require continuous high fidelity sensed data for real-time assessment and intervention (e.g., fall detection, heart arrhythmia detection, _etc._), which would be very costly to transmit wirelessly, reduction or elimination of wireless transmission may be necessary to meet longer battery life and wearability requirements. Such applications may need to make intervention/actuation decisions on-node and only employ wireless transmission when events of interest are detected. This system level design decision will help to reduce node power consumption sufficiently to satisfy the other system requirements. BSNs for delay insensitive applications, such as those employed by clinicians to gather information in large volume, may alternatively leverage lower power on-node storage, rather than wireless transmission, to increase battery life. Such store-and-forward use cases, including Holter monitoring and activity logging, are capable of acquiring high fidelity data for later assessment off-node. In such cases, on-node processing is limited, as more resource rich or expert assessments are made off-node. Finally, real-time applications involving wireless transmission and high fidelity data (e.g., gait analysis, activity monitoring, gaming, _etc._), combine on-node signal processing with radio management to meet battery life demands of hours to days. Value to the user will ultimately determine each technology's success. BSNs must effectively transmit and transform sensed phenomena into valuable information and do so while meeting other system requirements, such as energy efficiency. The value of a BSN therefore rests in large part on its ability to selectively process and deliver information at fidelity levels and rates appropriate to the data's destination, whether that is to a runner curious about her heart rate or a physician needing a patient's electrocardiogram. These disparate application requirements require the ability to aggregate hierarchical information and integrate BSN systems into the existing information technology infrastructure. Increased value of the BSN to the user will also increase user tolerance of non ideal wearability or other technological difficulties. In this paper, we describe methods for developing efficient hardware within the unique set of requirements of BSNs for different parts of the BSN application design space. Due to the ubiquitous and strict energy constraint on all BSNs, we focus on energy efficiency. In addition, the approaches for achieving energy efficiency in BSNs designed with COTS components sometimes differ from those designed with custom hardware, and this paper explores both paradigms. Finally, this analysis is done within the context of current and projected BSN applications and use cases. Figure 2: Broad design space for BSN, but size limits energy for all applications.
## 4 Data Transmission  
In this section, we describe the different tests that have been conducted to design the physical layer of the transmission system. The communication is simplex, _i.e._, the data from the sensor is only transmitted and no acknowledge is received, and the power transmission is kept very low for energy saving reasons. Moreover, it is not possible to install large antennas in the ASJI for environmental protection, so the final radiated power is very low. All this involves the design of an extremely robust reception system.
# Determining the Optimal Configuration of the Multi-Ring Tree for Bluetooth Multi-Hop Networks
## 4 Network Performance  
### Optimization Configuration of the Multi-Ring  
With \(N\) ranging from 3 to 100, all factorization cases were calculated to determine the optimal number of ring \(R\) and its corresponding number of nodes in each ring \(P\) for the multi-ring subnet. In order to cover the computation nodes from 3 to 8, the least constraint value of k is equal to 2. In the single-ring subnet, the resulting \(R\) is always equal to 1, and \(P\) is equal to \(N\).  
With \(k\) varying from 0 to 3, Figure 5 shows the performance of the optimal number of ring \(R\) for various \(N\). The \(k\) = 3 case achieves the worst performance compared to the other three cases. In addition, the \(k\) = 0, 1, and 2 cases achieve the same performance concerning the optimal number of rings.  
The number of nodes in a ring \(P\) is determined by the number of nodes \(N\) over the number of rings \(R\), and thus it is inversely proportional to \(R\). Figure 6 shows that the performance of the number of nodes in ring \(P\) increases as the number of k increases for various \(N\), and k = 0 achieves the least value of \(P\). In the \(k\) = 0 case, \(R\) is equal to \(P\), and this case achieves the optimal configuration among all other cases. However, it took more search cases than all other cases to locate the optimal \(R\) and \(P\) for various \(N\), as shown in Figure 7, and the single-ring case performs with the least computation overhead. The computation overhead is defined as the number of search times for a random input \(N\) to be decomposable according to the constraint \(k\) and to locate the optimal number of ring configurations. The number of search cases decreases as \(k\) increases. As seen from the simulation, a trade-off exists between the computation overhead and the set value of \(k\).  
With the min-path algorithm, the optimal multi-ring configuration can be determined and the corresponding (\(R\), \(P\)) sets with minimum path length generated. Figure 8 shows the average path length of the multi-ring for various \(N\), and the \(k=0\) case achieves the shortest path length compared to the other two cases. The \(k=2\) case achieves almost the same hop length performance as \(k=0\), and both cases achieve superior hop length performance compared to the single-ring case. In addition, the increment slope of the average hop length for the min-path algorithm is much smaller than the slope of the single-ring case increases, because the near symmetrical deployment of the multi-ring subnet can be achieved under the constraint \(k\).  
Figure 5: Optimal number of rings for various \(N\).  
Figure 6: Optimal number of nodes in a ring for various \(N\).
## 3 System Model and Problem Statement
### Problem Statement  
Achieving perpetual network operation and maximizing network information gathering are among the central concerns when designing clustering protocols for EH-WSNs. By guaranteeing that every EH-Sensor node can work in an energy neutral state, the perpetual network operation can be achieved. This is easy to study in single-hop cluster-based routing schemes, since each CH only needs to handle the intra-cluster traffic. However, it is more complex when adopting multi-hop communication model in clustered EH-WSNs because each CH has both intra- and inter-cluster traffic. Moreover, for the purpose of maximizing network information gathering, all CHs should independently transmit data to BS at their fastest rate in single-hop clustering. However, this is not suitable for multi-hop clustering since the data from the CHs farther from BS has to be relayed by the ones closer to BS. If CHs farther from BS have faster data transmission rates than the ones closer to BS, the data from these CHs can not be immediately forwarded by the ones closer to BS that may reduce the performance of real-time systems. Next, we theoretically analyze the problem of how to achieve perpetual network operation and maximum network information gathering in a multi-hop clustered EH-WSN.
## 3 Results
### Measured Acceleration as a Function of Source Intensity and Distance  
At a distance of 4 cm from the source in dermatome C7, acceleration decreased by 73.3% on average at a vibration intensity of \(\sim\)100 Hz and by 83.8% at \(\sim\)240 Hz. At a distance of 8 cm in dermatome C7, acceleration decreased by 86.7% on average compared to the source at \(\sim\)100 Hz, and by 96.2% at \(\sim\)240 Hz. At distances of 12 cm and 16 cm in dermatome C7, acceleration at all tested vibration intensities decreased to \(\sim\)0 G (i.e., less than the bit resolution of the accelerometer: \(6.1\times 10^{-5}\) G). In dermatome T1, acceleration was also negligible across all vibration frequencies at a distance of 8 cm (a maximum 0.02 G at \(>\) 230 Hz, a reduction of 99.2%) and decreased to 0 G at 16 cm for all vibrationintensities. At the UH (i.e., a distance exceeding 18 cm from the source vibration in all participants), acceleration decreased to 0 G at all tested vibration intensities.  
Compared to the ideal \"no-propagation\" value of 0 G, the Bonferroni-corrected, one-tailed t-test revealed significant differences between the measured accelerations at all vibration intensities for the measured distances of 4 and 8 cm in dermatome C7, and 8 cm in dermatome T1 (p\({}_{\text{corrected}}<0.05\) in each case). There were no significant differences at 12 and 16 cm in dermatome C7, at 16 cm in dermatome T1, and at the UH (p\({}_{\text{corrected}}>0.05\) in each case).  
To determine the extent to which the vibration propagation might interfere with vibration perception, we compared accelerations measured at each location to the amplitude of the vibrotactile intensity discrimination threshold in either dermatome C7 (\(\sim\)0.35 G), dermatome T1 (\(\sim\)0.50 G), or the UH (\(\sim\)0.40 G), respectively (c.f., [32]). In dermatome C7, the measured acceleration at 4 cm was not significantly lower than 0.35 G for intensities greater than 156 Hz (p\({}_{\text{corrected}}>0.05\)). This result was not a statistical artifact because the tests still showed non-significant differences for frequencies greater than 210 Hz when Bonferroni-correction was removed. Measured accelerations were significantly lower than the C7 discrimination threshold at all vibration intensities at distances of 8, 12, and 16 cm (p\({}_{\text{corrected}}\)\(<0.05\) in all cases). Measured accelerations were also lower than their respective discrimination thresholds at 8 and 16 cm in dermatome T1 and at the UH (p\({}_{\text{corrected}}<0.05\) in all cases).