esa-sceva/satcom-chunk-collection huggingface dataset:
# Energy Harvested and Cooperative Enabled Efficient Routing Protocol (EHCRP) for IoT-WBAN
###### Abstract  
The health industry is one of the most auspicious domains for the application of Internet of Things (IoT) based technologies. Lots of studies have been carried out in the health industry field to minimize the use of resources and increase the efficiency. The use of IoT combined with other technologies has brought quality advancement in the health sector at minimum expense. One such technology is the use of wireless body area networks (WBANs), which will help patients incredibly in the future and will make them more productive because there will be no need for staying at home or a hospital for a long time. WBANs and IoT have an integrated future as WBANs, like any IoT application, are a collection of heterogeneous sensor-based devices. For the better amalgamation of the IoT and WBANs, several hindrances blocking their integration need to be addressed. One such problem is the efficient routing of data in limited resource sensor nodes (SNs) in WBANs. To solve this and other problems, such as transmission of duplicate sensed data, limited network lifetime, etc., energy harvested and cooperative-enabled efficient routing protocol (EHCRP) for IoT-WBANs is proposed. The proposed protocol considers multiple parameters of WBANs for efficient routing such as residual energy of SNs, number of hops towards the sink, node congestion levels, signal-to-noise ratio (SNR) and available network bandwidth. A path cost estimation function is calculated to select forwarder node using these parameters. Due to the efficient use of the path-cost estimation process, the proposed mechanism achieves efficient and effective multi-hop routing of data and improves the reliability and efficiency of data transmission over the network. After extensive simulations, the achieved results of the proposed protocol are compared with state-of-the-art techniques, i.e., E-HARP, EB-MADM, PCRP and EERP. The results show significant improvement in network lifetime, network throughout, and end-to-end delay.  
Keywords:cooperative effort; energy harvesting; efficient data transmission; IoT, routing protocol; WBAN +
Footnote †: journal: Sensors
## 2 Methods
### Cross-Frequency Directionality  
The cross-frequency directionality (CFD) estimates the direction of interaction between the phase of low-frequency (\(f_{l}\)) oscillations and the amplitude of faster, higher-frequency (\(f_{h}\)) oscillations [11]. This is based on the phase slope index (PSI), which measures the coupling directionality between two oscillatory signals of similar frequencies [33]. Given two time series \(\mathbf{x}\) and \(\mathbf{y}\), the CFD from \(\mathbf{x}\) to \(\mathbf{y}\) is computed as the PSI between \(\mathbf{x}\), the time series containing the slow oscillations of interest, and the amplitude envelope of \(\mathbf{y}\) at \(f_{h}\) (\(\xi^{y}\)). Thus:  
\[CFD(\mathbf{x}\rightarrow\mathbf{y},f_{h})=PSI(\mathbf{x}\rightarrow\xi^{y})= \Im\Bigg{(}\sum_{f\in F}C_{\mathbf{x}_{\text{S}}}^{*}(f)C_{\mathbf{x}_{\text{S} }}(f+df)\Bigg{)}, \tag{4}\]where \(C_{\mathbf{x}\xi}=S_{\mathbf{x}\xi}/\sqrt{S_{\mathbf{x}\omega}S_{\xi\xi}}\) is the complex coherence, \(S_{\mathbf{x}\xi}\in\mathbb{C}\) is the cross-spectrum between \(\mathbf{x}\) and \(\xi^{y}\), \(S_{\mathbf{x}\xi},S_{\xi\xi}\in\mathbb{C}\) are the auto-spectrums of \(\mathbf{x}\) and \(\xi^{y}\), \(df\in\mathbb{R}^{+}\) corresponds to the frequency resolution, \(F\) indicates the frequency range over which the slope is summed, and \(\Im\) stands for the fact that only the imaginary part of the sum is selected [33]. Therefore, the CDF estimates the slope of the phase difference, between the phases of \(\mathbf{x}\) and \(\xi^{y}\), as a function of frequency. That is to say, it translates the problem of estimating the direction of phase-amplitude interactions to estimating interaction between phases.
## 2 Materials and Methods
### Measurements, Sampling Procedure, and Analytical Methods
#### 2.2.6 Volatile Organic Compound Determination  
The volatile organic compound (VOC) extraction was carried out using a solid phase microextraction (SPME) device (Supelco, Bellefonte, PA, USA), including a 10 \(\upmu\)m length fused silica fibre coated with a layer (50/30 \(\upmu\)m thickness) of divinylbenzene/carboxen/polydimethylsiloxane (DVB/CAR/PDMS), following the procedure described by [PERSON] et al. [PERSON] et al. (2016). Briefly, 1 g of minced mozzarella cheese was weighted in a 24 mL glass vial closed with a screw cap equipped with a laminated Teflon-rubber disc. The samples were kept at 35 \({}^{\circ}\)C for 15 min, allowing for the equilibration of VOC in the headspace. Then, the fibre, already conditioned at 270 \({}^{\circ}\)C for 60 min via heating in a gas chromatograph injection part, was inserted in the headspace of the vials through the septum and exposed to the headspace for 30 min at 35 \({}^{\circ}\)C. The splitless mode was used to inject the samples. Helium was used as carrier gas, with a linear velocity of 40 cm s\({}^{-1}\). The total run time of 49 min and 30 s was divided as follows: 10 min at 40 \({}^{\circ}\)C (isothermal), and then increased to 20 min at 0 \({}^{\circ}\)C (5 \({}^{\circ}\)C/min), and finally, 250 \({}^{\circ}\)C (20 \({}^{\circ}\)C/min), maintained for 5 min. Both the injector and detector temperatures were set at 260 \({}^{\circ}\)C. The gas chromatograph used for the VOC detection was 6890 N (Agilent Technologies, Santa Clara, CA, USA) equipped with a DB-624 capillary column (30 m, 0.25 mm i.d., 1.4 \(\upmu\)m film thickness; J&W Scientific, Folsom, CA, USA) and coupled to a mass selective detector, 5973 N (Agilent Technologies). VOCs (expressed as area units (AUs) \(\times\) 10\({}^{4}\) per g of product) were identified by comparing the detected mass spectra with those reported in the NIST05 library (National Institute of Standards and Technology, Gaithersburg, MD, USA) (matching was considered when coincidence was greater than 80%), and/or by comparing the mass spectra and retention times with the authentic standards (Supelco, Bellefonte, PA, USA), and/or by calculating the retention index against a set of standard alkanes (C5-C14) (for calculating Kovats indices, Supelco 44585-U, Bellefonte, PA, USA) and by comparing them with the data reported in the literature.
# Detection and Mitigation of RPL Rank and Version Number Attacks in the Internet of Things: SRPL-RP
## 1 Introduction
### RPL Routing Protocol Overview  
RPL was designed to support the vision of Wireless Sensor Networks (WSNs) and the Internet of Things (IoT) domains. RPL is also intended to be an interoperable and simple networking protocol to meet the requirements of resource-constrained devices and applications. RPL is a distance-vector routing protocol with its topology based on a Destination Oriented Directed Acyclic Graph (DODAG). The DODAG root is used to collect default routes to the internet and distribute them into other routing protocols. Each node in the topology is assigned a rank value that defines the position of the node with respect to the DODAG root. In addition, according to the specification of the RPL, each DODAG root node contains a version number. To invalidate the integrity of the DODAG root node and launch a global repair mechanism, a DODAG new version is formed by incrementing the version number that is determined by the root node only. The creation of the RPL network topology is maintained with five control messages, which are (1) DODAG Information Object (DIO), (2) DODAG Information Solicitation (DIS), (3) Destination Advertisement Object (DAO), (4) DAO Acknowledgement (DAO-ACK), and (5) Consistency Check (CC). Depending on the Objective Functions (OF), the RPL can nominate the optimal route to define the parent and neighbor node selection [5].
# FEL Pulse Duration Evolution along Undulators at FLASH
## 1 Introduction  
The electron bunches in single-pass, high-gain, free-electron lasers (FEL) propagate through the undulators just once producing the most intense extreme ultraviolet (XUV) and X-ray pulses. The amplification mechanism is the so-called self amplification of spontaneous emission (SASE) resulting in fundamental statistical fluctuations in the radiation properties. The properties of radiation pulse energy and radiation pulse duration have been studied theoretically and experimentally in the last decades for XUV and X-ray ranges [1; 2; 3; 4]. It turns out that the pulse energy (i.e., the number of photons in one ultrashort pulse) grows gradually by many orders of magnitude in the amplification process, whereas the pulse duration first decreases in the exponential stage of amplification and then grows by about a factor of two when the amplification process enters the nonlinear regime.  
The evolution of the pulse energy along the undulators, the so-called gain curve, has been measured frequently in the past [5; 6; 7; 8; 9; 10; 11]. However, to our knowledge, there has been only one study that investigates pulse duration in relation to the number of undulators contributing to the lasing [4]. In this study, however, pulse duration is only estimated by analyzing the spectral fluctuations for one X-ray wavelength. Here, we present a more comprehensive study where we measured the single-shot pulse duration as well as the pulse energy for six different wavelengths at the FLASH2 facility [11]. In addition to the average, we can analyze the single-shot data and provide experimental values for the shot-to-shot fluctuations. To obtain a complete picture of the process, the experimental data are compared with FEL simulations.
# Lip-Reading Classification of Turkish Digits Using Ensemble Learning Architecture Based on 3 DCNN
## 3 Material and Method
### Dataset Preprocessing
After extracting data from the videos, data augmentation was performed by rotating the data 10 degrees to the left and 10 degrees to the right. In this study, data augmentation was applied using rotation only to increase the variability and robustness of the dataset. Rotation was chosen as the augmentation technique because it effectively increases the variability of the dataset without compromising the natural features of lip movements. With these operations, Dataset1 and Dataset2 were ready for deep learning models before the training, testing and validation of the data. Grouped 5-fold cross-validation was used to divide the data into training, validation and test sets. Grouped 5-fold cross-validation ensures a fair evaluation for problems such as lip reading by preventing the same data from being present in both training and test sets. The dataset is divided into five groups according to their folder structure and in each iteration, one folder is assigned as the test set, while the remaining folders are used for training and validation. The dataset is first divided into a training set of 80% and a test set of 20%. A validation set of 20% was separated from the training set to be used in processes such as hyperparameter optimization and early stopping of the model. As a result of this  
Figure 4: Extracting the lip area during data preprocessing. \begin{table}
\begin{tabular}{c c} \hline
**Phase** & **Video Count** \\ \hline Original Dataset & 3900 \\ After Data Augmentation (Training) & 11,700 \\ \hline \end{tabular}
\end{table} 
Table 3: Number of videos in original and augmented datasets. splitting strategy, the total data set is organized as 64% training, 16% validation and 20% test set. The validation set was used only in the training process to evaluate and optimize the hyperparameter settings. The test set is reserved to measure the final performance of the model, completely independent of the validation process.
# A High-Resolution MIR Echelle Grating Spectrometer with a Three-Mirror Anastigmatic System
## 5 Conclusions  
In this paper, an immersed infrared echelle spectrometer was designed based on the remote sensing detection requirements of missile tail flames. By introducing an immersion grating, a compact structure and high resolution were achieved, resulting in a high-resolution infrared mid-echelle grating spectrometer with a focal length of 300 mm and a working band of 1. The optical design software Zemax was used for optimization and analysis. The results show that the spectral resolution was 0.25 cm\({}^{-1}\)@4.25 um at the center reference wavelength for a single CCD pixel. The energy collected internally was more than ~50-70%; the spectrometer system could image the full spectrum in the range of ~3.7-4.8 um on a two-dimensional array CCD with a detection area of 9.60 \(\times\) 7.68 mm, with no overlap of spectral orders. The present image presents a design example of an immersion echelle spectrometer that adopts a Littrow layout with a very compact optical system, achieving high spectral resolution, good image quality, and a compact overall structure. The performance of the designed echelle spectrometer was verified by simulating the parameters of several optical components. The proposed device has the potential to be used on airborne platforms for gas detection in missile defense and is more stable than existing infrared high-resolution spectrometers for space applications.  
Conceptualization, Q.W. and H.S.; methodology, W.L.; software, Q.W.; validation, Q.W. and W.L.; formal analysis, Q.W.; investigation, Q.W. and P.D.; resources, Q.W.; writing--original draft preparation, Q.W.; writing--review and editing, Q.W.; funding acquisition, H.S. All authors have read and agreed to the published version of the manuscript.  
This research was funded by The National Key Research and Development Program of China.  
Not applicable.  
The data are contained within the article.  
The authors declare no conflict of interests.  
Figure 12: Spot diagrams of three wavelength points with uniform distribution for each order.
# Analysis of the Accessibility of the Swinoujscie-Szczecin Fairway for Deep-Draught Ships
## 2 Study Area
### Navigational Constraints and Accessibility of the Fairway  
The Swinoujscie-Szczecin waterway supports large commercial vessels, including container ships, bulk carriers, and tankers, which carry cargo to the ports of Szczecin and Swinoujscie. Constraints due to the depth of the fairway and the variability of water levels pose a key challenge to the movement of deep-draught vessels, particularly during periods of low water levels.  
The fairway is also undergoing constant modernisation, including dredging and expansion, to meet the growing demands of maritime transport. In recent years, a number of works have been carried out to improve its accessibility for vessels with greater draught, which is particularly important given the increasing traffic of container vessels and specialised transport vessels.
# A Novel Mobile Communications Authentication Scheme with Roaming Service and User Anonymity
## 2 Preliminaries
### [PERSON]'s Scheme
#### 2.1.1 Registration Phase  
**R1.** MU\(\rightarrow\)HA: m\({}_{\text{LR1}}\)(ID\({}_{\text{MU}}\), H(PW\({}_{\text{MU}}\oplus\)r\({}_{\text{n}}\)))  
The MU chooses the identity ID\({}_{\text{MU}}\), password PW\({}_{\text{MU}}\), and a random number r\({}_{\text{n}}\); calculates H(PW\({}_{\text{MU}}\oplus\)r\({}_{\text{n}}\)); and sends the registration request message {ID\({}_{\text{MU}}\), H(PW\({}_{\text{MU}}\oplus\)r\({}_{\text{n}}\))} to the HA.
**R2.** HA\(\rightarrow\)MU: m\({}_{\text{LR2}}\)(ID\({}_{\text{HA}}\), E\({}_{\text{p}}\), E, n, P, P\({}_{\text{HA}}\), z, H(.)}  
When the registration request message {ID\({}_{\text{MU}}\), H(PW\({}_{\text{MU}}\oplus\)r\({}_{\text{n}}\))} is received from the MU, the HA calculates H(ID\({}_{\text{MU}}\mid\) 1 N) and z = H(PW\({}_{\text{MU}}\oplus\)r\({}_{\text{n}}\))\(\oplus\)H(ID\({}_{\text{MU}}\mid\) 1 N). Then, the HA sends ID\({}_{\text{HA}}\), E\({}_{\text{p}}\), E, n, P, P\({}_{\text{HA}}\), z, and H(.) to the MU.  
**R3.** MU: ID\({}_{\text{HA}}\), E\({}_{\text{p}}\), E, n, P, P\({}_{\text{HA}}\), z, H(.)  
After the message {ID\({}_{\text{HA}}\), E\({}_{\text{p}}\), E, n, P, P\({}_{\text{HA}}\), z, H(.)} is received from the HA, the MU stores this information along with r\({}_{\text{n}}\) onto the smart card. This completes the registration phase.
## 4 Conclusions  
In this paper, based on the STA algorithm, we see that this algorithm is improved, and the pixel selection threshold and pixel search direction suitable for a DEM image of the Gaosong ore field are set. In the process of pixel connection, a line segment is innovatively proposed, and its endpoints are taken to obtain a point feature connection between specific search thresholds. A lineament distribution map of the mining area is obtained, which is compared with the lineament results extracted based on the traditional Canny algorithm and the image data measured in the field. The experimental conclusions are as follows:  
(1) The attribute values (mean and standard variance) of fracture strength and fracture density in the extracted lineament results based on the improved line segment tracking algorithm are always closer to the measured data than the attribute values of fracture strength and fracture density in the extracted lineament results based on the Canny algorithm.  
(2) The strike data of lineaments extracted based on the improved line segment-tracking algorithm is closer to the strike data of the measured fracture distribution in the Gaosong ore field.  
(3) The improved algorithm extracts the image as a digital elevation image, which can reduce the influence of houses, roads, factories, and water bodies on the lineament extraction results in Landsat 8 Oli images to a certain extent.  
The above experimental results show that the improved line tracking method is not only faster than the traditional lineament extraction method in the field of lineament extraction, but also that the extraction results are more accurate and closer to the field data than the traditional method. The interference of external factors is also reduced, which undoubtedly provides a more efficient technical means for the analysis and research of mining area structures, making it more convenient for people to understand the formation process of geological structures in mining areas. However, there are still some problems, such as: a single line element cannot be identified as a linear structure, resulting in some fractures that cannot be identified; and some large faults cause linear structure connection discontinuity due to excessive turning. To solve these problems, continuing to optimize the linear structure connection program is the key to the next step.  
Conceptualization, formal analysis, writing--original draft preparation, J.F.; Manuscript review, C.N. All authors have read and agreed to the published version of the manuscript.  
This research was funded by projects (41562017, 40902058 and 42062020) supported by the NSFC, the project of Yunnan Major Scientific and Technological Projects (grant NO. 202202 AG050006).  
Data Availability Statement: Data sharing not applicable.  
The authors declare no conflict of interest.
# Battery Management System Hardware Concepts: An Overview
## 2 Battery Management System Requirements
### Balancing  
Several causes that lead to charge imbalance between serially connected battery cells can exist in a given system. Usually, a generic requirement would be to keep these low enough to not affect the system's performance and reliability. Depending on the individual application, however, there might be special aspects that have to be taken care of, e.g., resulting from weight constraints (see, e.g., Section 3), or required charging currents that lead to balancing currents. Section 5 provides further information on the need to implement balancing and usual realization methods.
# Graph-Driven Deep Reinforcement Learning for Vehicle Routing Problems with Pickup and Delivery
## 5 Experiments
### Comparison Analysis  
#### 5.2.1 Baselines  
We compare the proposed GDRL with a variety of heuristic and DRL-based methods as follows:  
* CPLEX [31]: the most powerful decision optimizer for mathematical programming problems. We employ CPLEX 20.1 to solve VRP-PD and set the maximum computing time as 3600 s (1 h).
* LKH [23]: a well-known efficient solver for TSP, CVRP, and its variants. Similarly, we adopt LKH to handle test instances with a maximum time limit of 3600 s.
* GA [32]: an evolutionary computation-driven genetic algorithm that utilizes mutation and crossover operators to yield solutions for VRPs.
* SA [33]: the simulated annealing approach that models the VRP-PD as a mixed-integer programming issue and delivers satisfactory results for routing.
* ACO [34]: an ant colony optimization method leveraging swarm intelligence to search excellent route solutions for the conventional and improved VRP-PD.
* ALNS [35]: an adaptive local neighborhood search algorithm for various routing problems.
* AM [24]: an attention model that leverages a transformer-based encoder-decoder framework to tackle TSPs, CVRPs, and VRP-PDs and yield high-quality solutions.
* POMO [25]: a DRL-based method that learns diverse multiple optimal policies and achieves state-of-the-art routing performance on various VRPs.
* FER [26]: a learning-based approach that leverages a novel encoder-refiner-decoder framework for feature embedding and route construction. It exhibits superior solutions for classical VRPs.