esa-sceva/satcom-chunk-collection huggingface dataset:
# Radio Frequency Fingerprint Identification for 5G Mobile Devices Using DCTF and Deep Learning
## 4 Experimental Results
### Performance of the Index-Based DCTF Identification Scheme  
The index-based DCTF identification scheme was tested for Scenario 1. The CNN model was trained for each type of sequence among the 27 preamble sequences. In order to simulate the case where complete knowledge of all PRACH signals was available at the gNB, the testing set was established using the same 27 preamble sequences. The collected signal samples were randomly assigned into the training and testing sets at a ratio of 8:2. The data in the training and testing sets had no intersection.  
The classification results are presented in Figure 13. UE2 and UE4 showed an accuracy of around 99%. The high accuracy of these two devices was due to the fact that their models were different from the others. On the other hand, the other four devices belonged to the same model, and thus their RFF features were quite similar, which resulted in a lower classification accuracy. In conclusion, when the gNB had complete knowledge of the PRACH preamble sequences, the classification accuracy for the six devices was 92.78% using a multi-channel CNN.  
Additionally, the performance of the single-channel CNN was also tested, and the results are presented in Figure 14. It can be observed that the classification accuracy of five devices slightly decreased, and the overall accuracy was 92.28%. This result demonstrates the advantages of the multi-channel CNN for Scenario 1, which emphasizes the transient features.
# Development of Patient Specific Conformal 3D-Printed Devices for Dose Verification in Radiotherapy
## 3 Results and Discussion  
Radiotherapy aims at irradiating a delineated tumor while minimizing the radiation dose delivered to the surrounding healthy tissues as much as possible. High energy (MeV) photons exhibit a skin-sparing effect as a result of the build-up region. This is useful when treating deep-seated tumors in a body, avoiding undesired early and late reactions of the skin. Nevertheless, if the tumor is superficial, the skin-sparing effect leads to tumor underexposure and higher risk of recurrence [7]. In order to overcome the skin-sparing effect in radiotherapy treatment, the application of 3D-printed bolus/compensating structure was explored in this study. It should be noted that since analysis was performed on a real patient with head and neck cancer, a 3D-printed patient-specific phantom was also needed for in-vitro dosimetric applications. Characterization and evaluation of feasibility of 3D-printed devices for their potential use in clinical applications has been performed focusing on three main aspects: tissue equivalency, geometric accuracy and conformity of the objects with the shape of the patient's surface, and in vitro dose verification.
# A Novel Energy-Efficient Contention-Based MAC Protocol Used for OA-UWSN
## 5 Simulation Results  
### Simulation Parameters  
The performance evaluation of the proposed MAC protocol of the OA-UWSN was conducted on the OMNeT++ platform. The OA-CMAC protocol was compared to the OA-MAC [36] protocol based on the OA-UWSN proposed by our research group. The performance was evaluated on metrics including throughput, energy consumption, and other indicators evaluating protocol performance.  
The settings of the system simulation parameters are shown in Table 4.
# An Improved Channel Estimation Technique for IEEE 802.11p Standard in Vehicular Communications
## 3 Overview of Current Channel Estimation Schemes
### Scenario 2: Remain the Structure of the IEEE 802.11p
#### 3.2.6 CDP Based Scheme  
In [5], the preamble-based channel response is utilized to construct data pilots in subsequent data symbols. The high correlation characteristics of the channel response between the adjacent data symbols are employed by exploiting the four-phase tracking pilots to achieve better performance than STA. The estimation process in the CDP scheme is composed of five steps, including (1) equalization; (2) constructing data pilot; (3) LS estimation; (4) demapping; and (5) comparison. The first three steps in the CDP scheme are similar to the STA scheme. In the next two steps, the high correlation characteristic is utilized to achieve performance improvement over STA and decision directed schemes, especially in the higher SNR region.  
Table 3 presents the comparison of the discussed schemes. The Computational Complexity Level is represented by CCL, and the BER Performance Level is represented by PL. CCL 0.1 means very low computational complexity (e.g., LS) and CCL 0.5 means very high computational complexity (e.g., GDPS and DD). Please note that this grading is for understanding the difference between the computational complexities of various schemes. PL 0.1 means excellent performance and 0.5 means a severe performance degradation (e.g., LS). The terms SM and CSI denote System Modification and Channel State Information respectively in Table 3.
## 5 Numerical Simulation Results
### Parameter Setting  
A micro-grid in a certain area is taken as an example which possesses 10 wind turbines and 150 photovoltaic arrays, and the simulation schematic diagram is shown in Figure 5. The rated power of a single wind turbine is 100 kW, and the corresponding cut-in wind speed, cut-out wind speed, and rated wind speed are 3.5, 25, and 15 m/s respectively. The photovoltaic array consists of 4 \(\times\) 5 photovoltaic panels, with a rated power of 4 kW. A total of 2000 EVs are simulated in numerical simulations, and the relevant parameters are shown in Table 2. The charging mode of EVs is slow (AC) charging and users charge EVs through the public charging piles of EV cluster aggregators. In addition, the daily load of the micro-grid and the peak-valley price are shown in Tables 12 and 13 in the Appendix A. Above parameters and data used are based on the existing literature and the actual micro-grid project [28; 29; 30]. It should also be noted that the cost of generation and transmission of renewable energy is mainly composed of the generation cost of wind power and photovoltaic power, price inclusive tax and transmission price. This paper sets the generation and transmission cost for each period, as shown in Table 14 in the Appendix A[28]. At the same time, the micro-grid needs to purchase electricity from the external power system when the power generation of distributed generators in the micro-grid cannot meet the demand of basic load and EVs charging load. The electricity price of the external power system is shown in Table A4 in the Appendix A.
# Amphishea: A Novel Persistent Buffer Management Strategy to Improve SMR Disk Performance
## 5 Amphisbaena Design
### Overhead and Reliability Analysis  
This section discusses the space and time overhead of Amphishea and how to ensure data consistency in the event of a power failure.  
**Space and time overhead.** As shown in Figure 10, different data structures are used to organize metadata in the HFB and LFB, which inevitably introduces some space and time overhead. As described in Section 5.2.2, block-level mapping and chunk-level mapping are used to manage the address mapping information of HFBs and LFBs. In addition, the bitmap is used to keep track of the validity of the blocks in a chunk. As mentioned in Section 5.2.3, the size of the HFB is limited to 25% to 75% of the total persistent buffer capacity. Therefore, for a system with 64-bit addressing, given chunk size = 64 KB and block size = 4 KB, the block-level and chunk-level metadata space overhead with per-GB persistent buffer is at most 29 KB and 4 MB, respectively. In a real environment, while the memory consumption of metadata is proportional to the size of the persistent buffer specified for initialization, this can be flexibly adjusted at runtime depending on the resource utilization of the system. For example, with a small persistence buffer, all metadata can be loaded into memory; conversely, with a large persistence buffer, limited system resources allow only some of the most frequently accessed metadata to be loaded into memory. In terms of the time overhead, HFB and LFB use hash and a balanced binary search tree to locate the mapping information of the target block, and their time overheads are O(1) and O(log(n)), respectively, where n is positively correlated with the number of chunks.  
**Reliability.** Storing metadata in the RAM space can speed up response to the I/O requests, so the metadata is loaded into the cache on demand. In the event of a power failure, metadata that has not been synchronized promptly will be lost, resulting in data inconsistency issues. This requires periodic synchronization of metadata in memory with the CMR zones on the disk. Today, many advanced solutions can be used to ensure data consistency, such as write-ahead logs or the use of small non-volatile memory (NVM), which can be used to ensure data consistency. Designing metadata caching strategies and data consistency strategies is beyond the scope of this paper, and we will focus on these issues in the future.
# Efficient Key Exchange Using Identity-Based Encryption in Multipath TCP Environment
## 5 Experimental Evaluation of Proposed Model
### Security Evaluation  
The proposed model, SKEXMTCP, uses the IBE technique to encrypt the session keys being exchanged during the initial handshake. In order to encrypt the session keys using IBE, the client uses the IP address as a public key, and the server obtains the corresponding private key from the PKG to decrypt the session keys. Here, the server authenticates itself by using the IP address and port number, which will be encrypted by the public key of PKG and digitally signed by the server. If an attacker tries to find out the private key of PKG to decrypt the packet and tries to change the digital signature of the server to forge the packet, it is required to break the encryption algorithm and hashing algorithm. Thus, the security complexity of the model relies upon the complexity of IBE and the encryption algorithm used for encrypting the private key request packet. Table 5 shows the comparison of whether the various solutions offer security against various attacks or not.
## 3 Proposed Methodology
### _Rfo-Svm_  
The RFO-SVM algorithm offers several advantages for data processing efficiency, scalability, improved performance, enhanced accuracy, flexibility, and optimized resource utilization. RFO-SVM enables parallel processing, distributing the computational load across multiple resources and reducing processing time by horizontally fragmenting large datasets. The RFO-SVM is applied to an input database after pre-processing to ensure data completeness and to analyse the features and target labels of the database, classifying them based on their respective class labels. The parameters are optimally selected like the kernel, and boundary parameter values of the SVM algorithm. The selection process is performed using the RFO algorithm, which utilizes accuracy metrics to optimize the parameter values. The classified data are referred to as fragments to achieve accurate fragmentation; the fragments are allocated to different nodes within a DDBMS. This allocation is performed using a standard Genetic Algorithm (GA), with the total allocation cost serving as the fitness function to ensure effective fragmentation and allocation of the database. In the case of SVM, accuracy is considered as a trade-off between complexity (number of support vectors--NSV) and margin (M). This load balance is controlled by selecting the SVM configuration, including the choice of M, kernel type, and kernel parameters. To address this trade-off, the conflicting objectives of the optimization problem are determined as accuracy and model complexity. Model complexity is represented by the number of support vectors (NSV).  
\[\min F(X)= |f_{1}(x),\ f_{2}(x)\,| \tag{19}\]  
where  
\[f_{1}(x)=accracy \tag{20}\]  
\[f_{2}(x)=NSV \tag{21}\]  
This equation is resolved to provide the solution for RFO-SVM configuration and this will be the main function of the proposed optimization framework.
## 3 DL-Based Ontology  
A DL-based ontology generally consists of a DL terminology box (TBox), a DL assertion box (ABox), and a set of SWRL rules. The DL TBox is a collection of concepts, relations, and definitions of concepts (terminological axioms). A set of concept assertions, relation assertions, and instance assertions (assertional axioms) constitute the DL ABox. The SWRL rules are used to describe the knowledge that cannot be described by DL. In this section, a DL-based ontology for representation of the LPBF process planning knowledge is developed using Protege [53] according to standard terminologies for LPBF (ISO/ASTM 52900, 2021), LPBF process planning guidelines (ISO/ASTM 52911-1, 2019; ISO/ASTM 52911-2, 2019), database of LPBF materials [54], database of LPBF machines [54], and existing related research results [4; 5; 6; 7; 8]. The schematic diagram of this ontology is shown in Figure 2. Benefiting from the advantages of the developed ontology, the semantics of LPBF process planning data are enriched greatly, and consistency checking, knowledge reasoning, and semantic query, which are not available in current process planning tools for LPBF, can be performed via DL, DL, and SWRL, and DL, SPARQL, and SQWRL, respectively.  
In the following subsections, the details of the ontology will be described in a top-down manner. For the sake of clarity, the following labelling convention will be adopted: all entity names are in italics (e.g., _LpbfMachine, isBasedOn, cosintM270_); the first letters of all DL concept (OWL class) names are in capitalised case (e.g., _Lpbf, ProcessPlanning_); all DL relation (OWL property) names have a prefix of 'has' or 'is' (e.g., _hasEnergySource, isApplicableFor_); the first letters of all DL instance (OWL individual) names are in lower case (e.g., _tiGAl4V_, _alSi10 Mg_).
# Systematic Error Correction for Geo-Location of Airborne Optoelectronic Platforms
## 5 Conclusions  
This paper proposes a systematic error correction method based on flight data. First, based on the kinematics characteristics of the airborne optoelectronic platform, the geo-location model was established. Then, the error items that affect the geo-location accuracy were analyzed. The installation error between the platform and the POS was considered, and the installation error of platform's pitch and azimuth was introduced. After ignoring higher-order infinitesimals, the least square form of systematic error is obtained. Therefore, the systematic error can be obtained through a series of measurements. Both Monte Carlo simulation analysis and in-flight experiment results show that this method can effectively obtain the systematic error. Through correction, it can not only reduce the root-mean-square value of the geo-location error, but also make the mean error closer to 0, making preliminary preparations for filtering further.  
The method proposed here mainly focuses on the systematic error correction for airborne optoelectronic platforms with laser rangefinders, which have multiple rotation axes such as azimuth and pitch axes. Through this method, we can not only get the misalignment between the platform and the body coordinate system established by the inertial instruments, but the systematic error of the rotation axis inside the platform. As for more rotation axis platforms, we just need to add more systematic error items and expand the equation. There are differences between this method and the common boresight calibration: first, this method introduces more calibration items than just misalignment between imaging coordinate system and body coordinate system with inertial instrument; second, this method applies to platforms with laser rangefinders to measure the distance between the imaging coordinate system and targets, which is different from getting boresight angles from images.  
This method can be widely used in the systematic error correction and geo-location of relevant photoelectric equipment. The next step we will focus on how to filter and locate both stationary and moving targets in real time after systematic errors are corrected.  
[PERSON], [PERSON], [PERSON] and [PERSON] initiated the research. [PERSON] designed the experiments and wrote the paper. [PERSON] contributed analysis tools. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
**Acknowledgments:** We acknowledge Academic Editor for his careful revision of the languages and grammatical structures in this article.
# Genetic Algorithm for the Retailers' Shelf Space Allocation Profit Maximization Problem
## 4 Problem Formulation
### Shelf Type Constraints  
The following shelf type constrains have been assumed:  
\[\forall(j:p_{j}^{pl}=0)\forall(i:s_{i}^{pl}=1)[x_{ij}=0],\;j=1,\ldots,P,\;i=1, \ldots,S \tag{5}\]  
\[\forall(j:p_{j}^{pl}=1)\forall(i:s_{i}^{pl}=0)[x_{ij}=0],\;j=1,\ldots,P,\;i=1, \ldots,S \tag{6}\]  
\[\forall(j:p_{j}^{pl}=1)\forall(i:s_{i}^{e}=0)[x_{ij}=0],\;j=1,\ldots,P,\;i=1, \ldots,S \tag{7}\]  
\[\forall(j:p_{j}^{lw}=1)\forall(i:s_{i}^{lw}=0)[x_{ij}=0],\;j=1,\ldots,P,\;i=1,\ldots,S \tag{8}\]  
\[\forall(j:p_{j}^{p}=1)\forall(i:i\
eq s^{v})[x_{ij}=0],\;j=1,\ldots,P,\;i=1, \ldots,S \tag{9}\]  
\[\forall(j:p_{j}^{lc}=1)\forall(i:i\
eq s^{lc})[x_{ij}=0],\;j=1,\ldots,P,\;i=1,\ldots,S \tag{10}\]  
Constraints (5) and (6) indicate whether the product is to be placed on a pallet. If the product must not be placed on a pallet (\(p_{j}^{pl}=0\)) or shelf type is not a pallet (\(s_{i}^{pl}=0\)), then \(x_{ij}=0\). The product may be placed on a pallet if it is assigned to the pallet (\(p_{j}^{pl}=1\)) and the shelf type is a pallet (\(s_{i}^{pl}=1\)).  
Constraint (7) demonstrates whether the product must be placed at eye level. If the product must be placed at eye level (\(p_{j}^{e}=1\)) and the shelf is not at eye level (\(s_{i}^{e}=0\)), then the product cannot be placed there. Other products can be placed at eye level too, so there are no restrictions for the products that are not assigned to the eye level (\(p_{j}^{e}=0\)).  
Constraint (8) shows whether the product must be placed at a lower level. If the product must be placed at a lower level (\(p_{j}^{lw}=1\)) and the shelf is not at a lower level (\(s_{i}^{lw}=0\)), then the product cannot be placed there. Other products can be placed at a lower level too, so there are no restrictions for the products that are not assigned to the lower level (\(p_{j}^{lw}=0\)).  
Constraint (9) signifies if the product must be placed on the shelf where the virtual segment for convenience products exists. If the product must be placed in the virtual segment for convenience products (\(p_{j}^{v}=1\)) and the shelf does not contain convenience virtual segments (\(i\
eq s^{v}\)), then the product cannot be placed on this shelf. If there is enough space on the shelf, regular products may also be placed here, so there are no restrictions for the products that are not assigned to the convenience zone (\(p_{j}^{v}=0\)).  
Constraint (10) indicates whether the product must be placed on the shelf where the virtual segment for local products exists. If the product must be placed in the virtual segment for local products (\(p_{j}^{lc}=1\)) and the shelf does not contain a local virtual segment (\(i\
eq s^{lc}\)), then the product cannot be placed on this shelf. If there is enough space on the shelf, regular products may also be placed in this zone, so there are no restrictions for the products that are not assigned to the local zone (\(p_{j}^{lc}=0\)).
# A Bi-LSTM Based Ensemble Algorithm for Prediction of Protein Secondary Structure
## 2 Materials and Performance Measure  
### Datasets  
Our training set contains non-homologous protein sequences extracted from the Protein Data Bank (PDB) database ([[http://www.rcsb.org/pdb/home/home.do](http://www.rcsb.org/pdb/home/home.do)]([http://www.rcsb.org/pdb/home/home.do](http://www.rcsb.org/pdb/home/home.do))). To guarantee the generality of our model, it is different from conventional methods whose training sets are generally 25% sequence similarity cutoff. The redundancy of our training data is first reduced at the 25% sequence similarity level, and then further reduced by using a sensitive search tool lightning-fast iterative protein sequence searching by HMM-HMM alignment (HHBits) to exclude all proteins that have a weak hit (E \(<\) 0.1), resulting in 5530 protein sequences. The initial size of the training set was 7720 protein sequences. The training set excluded proteins with chain breaks and those shorter than 35 residues.  
A 10-fold cross validation was applied for the dataset to train the ensemble model. The ratios of the three states (\(\alpha\)-Helix, \(\beta\)-sheet, coil) of amino acid residues in the training set are 33.7%, 27.6% and 38.7%, respectively. We used three publicly available datasets (513 protein Cuff & Barton set (CB513), data1199 and 203 proteins from Critical Appraisals Skills Programme (CASP203)) to evaluate the performance of our method. The CB513 dataset [17], containing 513 non-homologous protein sequences, is often used as the benchmark dataset to evaluate the performance of prediction methods about protein structures. The second test set came from Ref [18], including 1199 non-redundant sequences; data1199 hereafter. The third test set was derived from the 2016 CASP meeting, containing 203 non-redundant proteins, named CASP 203. To ensure the validity of the test result, no two sequences in the training and test sets have a similarity over 30%.
# Electrical Equalization Analysis of PAM-4 Transmission in Short-Reach Optical Systems
## 4 Results and Discussion
### Part 1--Conventional Model  
The first part relates to the \(f_{c}\) impact and details an analysis with the FFE and DFE on a conventional system model as described above and in Figure 1.  
We specified the \(f_{c}\) of one of the sides, transmitter (TX) or receiver (RX), to a normal value of \(f_{c}=0.7R_{s}\), where \(R_{s}\) is the symbol rate (\(baud\)), and changed the other side's \(f_{c}\). This test was meant to separate the ISI that was created in the TX from the one created in the RX because it is critical to discern the difference between them. Due to the square operation of the photodiode, the ISI that is caused by the TX filter and the fiber CD can be considered as \"non-linear\" ISI. However, at the output of the RX filter, until the equalizer input, we can consider the system as linear, and so is the ISI. We expected that in a case of narrow filter in the TX, the \"non-linear\" ISI would stifle the equalizer compensation in comparison to a case with an ideal filter width.  
Figure 5 shows the opposite.  
The orange curve shows that changing the TX filter has a negligible impact on the performances, compared to the RX changes, in the blue curve, that had a more considerable impact. In practice, we obtained better results with the narrow RX filter, and there is a 1.6-1.8 dB penalty for using the wider filter in the receiver. At this point, we assumed that the noise filtering in the RX side, which contributes to the filter narrowing, contributes to the improvement of the results.  
Another relevant explanation for the minor impact of the TX's filter changing is the dual influence of the BW. On the one hand, a narrower filter produces more expansion in the time domain and creates ISI. On the other hand, at the narrow filter output, we obtain the signal with lower frequency ingredients; therefore, the CD at the fiber has less impact on the signal expansion, so we obtain less ISI.
# Joint Cell Association and User Scheduling in Carrier Aggregated Heterogeneous Networks
## 2 Problem Statement  
In this section, a two-layer heterogeneous system overview with multiple available aggregated carrier bands is set up first, then both the network model and the signal model are introduced in detail. In order to improve the overall network capacity, a joint cell association and, finally, the carrier aggregation for traffic load balancing problem is formulated.