esa-sceva/satcom-chunk-collection huggingface dataset:
# Anti-Jamming Method and Implementation for GNSS Receiver Based on Array Antenna Rotation
## 3 The Proposed Antenna Rotation Method
An example of numerical calculation is given to help illustrate the proposed antenna rotation method. Take the four-element central circular array as an example. The element locations are depicted in orange circles in Figure 2, and the element positions are given in Table 2. Figure 3a,b illustrate the _ICR_ and residual interference power against rotation angle. There are six interferences in Figure 3. The incident angles in azimuth are \(28^{\circ}\), \(9^{\circ}\), \(-2^{\circ}\), \(-42^{\circ}\), \(-56^{\circ}\), and \(-62^{\circ}\), respectively. In order to simplify the antenna pattern, the incident angles in elevation are \(15^{\circ}\). The interference _ISR_ (jam-to-signal ratio) are 60 dB. Because of the symmetry of the aforementioned array geometry, only the trend in the rotation angle \(0^{\circ}\)-\(60^{\circ}\) is given here. The curve is extended periodically in the range of \(0^{\circ}\)-\(360^{\circ}\). It can be seen that at the rotation angle of \(19^{\circ}\), the _ICR_ reaches the optimal value and the interference residual power is the smallest. On the contrary, at the rotation angle of \(47.7^{\circ}\), the _ICR_ reaches the minimum and the residual interference power is the greatest. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Number of Elements** & **X** & **Y** & **Z** \\ \hline element 1 & 0 & 0 & 0 \\ element 2 & \(\lambda_{c}/2\) & 0 & 0 \\ element 3 & \(-\lambda_{c}/4\) & \(-\sqrt{3}\lambda_{c}/4\) & 0 \\ element 4 & \(-\lambda_{c}/4\) & \(\sqrt{3}\lambda_{c}/4\) & 0 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Element positions of four-element central circular array. Figure 2: Antenna array rotation diagram. Figure 3c,d shows the antenna pattern at \(\Delta\varphi=19^{\circ}\) and \(\Delta\varphi=47.7^{\circ}\), respectively. The red dashed line indicates the equivalent incident angle after antenna rotation. In spite of both of the patterns having four nulls, the nulls in Figure 3c are formed closer to the incident directions while, in Figure 3d, the incident directions have a higher gain. If there are enough satellite signals available, the eigenspace spanned by satellite signal steering vectors is similar to the unity matrix. The optimal anti-jamming performance can be obtained if the optimal rotation angle is determined.
## 4 Simulation Results  
The simulation results compare the performance of the proposed algorithm with those of existing schemes. For this purpose, the proposed algorithm will be compared to existing JP-CoMP clustering methods, in this case novel static clustering [13], coalitional game theory [19], affinity propagation (AP) [26], and capacitated affinity propagation (CAP) [27]. The user throughput, network scalability and complexity are evaluated in the simulation results.
## 5 Chicane Commissioning
### Data Analysis  
The sample sets of the dipole magnet currents are set up for momentum dispersion measurement. Thus, the momentum dispersions are obtained from different sample sets of the dipole magnet currents for data analysis. Figure 7 shows a plot of the momentum dispersion as a function of sample sets of the dipole magnet currents for a bunch charge of 50 pC. Note that the sample currents \(D_{1}=-D_{3}\) and \(D_{4}=-D_{2}\) are plotted side by side as they correspond to the same momentum dispersion. The relationship between the dispersion and dipole currents suggests the dipole magnet current setting with zero dispersion by the use of linear regression fittings. Thus, the dipole magnet currents \(D_{1}=-D_{3}=1.17\) A and \(D_{4}=-D_{2}=1.20\) A are obtained for 17.05 MeV/c beam to achieve zero dispersion.  
This method is repeated for beam momentum of 15, 16, 17, 18, 19, and 20 MeV/c in order to determine the dipole magnet currents to achieve  
Figure 6: Plot of beam vertical position \(\langle y\rangle\) at HIGH2.SCR3 as a function of beam momentum around 17.05 MeV/c for the chicane current \(D_{1}\) of 1.22 A, where error bars represent the root-mean-squared (rms) beam size.  
Figure 7: Plot of measured momentum dispersion as a function of sample sets of the dipole magnet currents for 17.05 MeV/c beam.  
shows a plot of the dipole magnet currents (with the zero dispersion) as a function of beam momentum with linear regression fitting results for a bunch charge of 50 pC. Therefore, the fitting results or interpolations will be applied for other beam momenta in any bunch compressor application at PITZ.
## Abbreviations  
The following abbreviations are used in this manuscript:  
HAPSs High-altitude platform stations GA Genetic algorithm NN Neural network CC Classifier chain CDF Cumulative distribution function 4G LTE Fourth-generation long-term evolution 5G NR Fifth-generation new radio UAVs Unmanned aerial vehicles RTT Round trip time BSes Base stations
# Priority Multi-Server Queueing System with Heterogeneous Customers
## 7 Conclusions  
In this paper, we investigated the multi-server priority queueing system with correlated flow of two types of customers. Such kind of systems practically is not investigated in the existing literature. The type of a customer defines its priority and distribution of the required service time. The system is analysed under quite general assumptions about the arrival and service processes. We calculated the stationary distribution of system states and the main performance measures including the probability of losses due to the lack of free servers at an arrival moment and due to forcing out of service a non-priority customer with priority ones. We conducted numerical experiments that showed the influence of the mean arrival rate and the effect of correlation in the input flow on the system performance measures. It is evidently seen that the use of the superposition of the stationary Poisson processes as a model of arrival flow leads to the significantly redundant optimism in prediction of the values of the main performance indicators of the system. This is unacceptable in real life applications and justifies the necessity of the provided mathematical analysis of the system.  
The used methodology of analysis (primarily the methodology for constructing the structured generator of multi-dimensional Markov chain) looks to be suitable for extension to the systems with more than two priority classes. The results seem to be also extendable to the systems with finite or infinite buffers. The results can be used to model modern telecommunication networks where the flows of information may be essentially heterogeneous with respect to the required bandwidth, importance for the system and tolerance to the losses and (or) delay and jitter.  
Conceptualization, V.K., A.D. and V.V.; methodology, V.K. and A.D.; software, A.D. and V.V. ; validation, A.D. and V.V.; formal analysis, V.K.; investigation, A.D.; writing, original draft preparation, V.K. and V.V.; writing, review and editing V.K. and A.D.; supervision A.D. and V.V.; project administration, V.K. and A.D. All authors have read and agreed to the published version of the manuscript.  
This work has been supported by grant No 19-29-06043 of the Russian Foundation for Fundamental Research.  
The authors declare no conflict of interest.
## 4 Experiments
### Experimental Results
#### 4.3.2 BGL Dataset Evaluation  
Table 3 compares the performance of LTAnomaly and the six baseline models on the BGL dataset, with the highest score expressed in bold. LTAnomaly has the best accuracy, with an F1-measure of 0.975, a 4% increase over CL2 MLog. The detection accuracy of LogCluster is not high, due to the high-dimensional sparsity of the event count matrix. Hence, it is difficult to distinguish between anomalies and normal situations with log clusters, leading to many false predictions. DeepLog only considers the temporal characteristics of log sequences and ignores the semantic characteristics of log messages, resulting in worse performance than LTAnomaly. The LogLS model performs better on HDFS and shows a poor performance on BGL datasets, indicating that it has a small application range. As the experimental results show, LTAnomaly performs well on both HDFS and BGL, indicating a wide range of applications.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Model** & **Number of Logs** & **Time Consumption** \\ \hline Deeplog & 787,095 & 2 h 17 m 29 s \\ HitAnomaly & 787,095 & 4 h 29 m 56 s \\ LTAnomaly & 787,095 & 3 h 22 m 6 s \\ \hline \hline \end{tabular}
\end{table}
Table 2: Time performance comparison of different anomaly detection models.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Techniques** & **Precision** & **Recall** & **F1-Measure** \\ \hline DeepLog & 0.91 & 0.71 & 0.797 \\ LogCluster & 0.42 & 0.87 & 0.541 \\ LogRobust & 0.91 & 0.78 & 0.840 \\ HitAnomaly & 0.95 & 0.90 & 0.924 \\ CL2 MLog & 0.91 & 0.97 & 0.939 \\ LogLS & 0.68 & **0.99** & 0.809 \\ LTAnomaly & **0.97** & 0.98 & **0.975** \\ \hline \hline \end{tabular}
\end{table}
Table 3: BGL dataset for evaluation.  
In the experiments, we examined the effects of window size and number of layers on the performance of our model. We tested the BGL dataset, changing one parameter value and setting the others to default values.  
The partition of the log sequence is determined by the window size. To investigate the influence of window size on the model, we set it to 5, 10, 15, 20, and 25. As we can see from Figure 9, LTAnomaly has relatively stable accuracy for different window sizes, with a certain influence on recall. With an increased window size, the efficiency of LTAnomaly anomaly detection does not show significant degradation, and it remains high. However, too small a window results in low recall, so the model cannot detect some anomalies. The model performs best with a window size of 15. The number of LSTM layers is also important for LTAnomaly. To observe its influence on LTAnomaly, we set the number of layers as 1, 2, 3, 4, and 6. Figure 10 shows its influence on LTAnomaly, from which we can find that the anomaly detection of the model is basically the same when the number of layers is greater than or equal to 2. However, the more parameters there are, the longer the model training and detection times. Hence, we prefer two LSTM layers.  
Figure 10: Influence of LSTM layers.  
Figure 9: Effect of window size.
## 2 Related Work
### Transfer-Learning-Based Methods  
The goal of transfer learning [39] is to solve the lack of annotated data in the source domain. [PERSON] et al. [40] investigated the migration capabilities of multiple deep recurrent neural network (RNN) presentation layers and used representation sharing at many levels to establish a single framework to manage cross-application, cross-language, and cross-domain migration activities. Their model performs better than the LSTM-CRF model on a public dataset for the named entity recognition (NER) task. There is a scarcity of Chinese NER labeling data. The problem can be handled utilizing an adversarial transfer learning strategy that fully uses the analogies between NER task boundary information and Chinese word segmentation, according to one paper [41].  
Machine learning models can only process text input that has been transformed into a digital matrix form using the word embedding approach. Researchers discovered that pre-training with a large amount of text data produces a better vector representation of the data than training with limited datasets [42; 43]. However, early word embedding approaches, known as static word vectors, such as Word2 vec and GloVe, are incapable of addressing the polysemy problem. The training result is a static word vector matrix that cannot be altered dynamically, making it difficult to understand the semantics of the text accurately. The pre-trained language model (PLM) trains word vectors in two stages, first using the neural network language model, and then fine-tuning according to downstream tasks. In order to obtain better results, ELMO [44] utilized bi-directional long short term memory (Bi-LSTM) as a feature extractor in the beginning stages to extract bidirectional text information. To extract one-way text information in generative pre-training (GPT), a transformer feature extractor is used. The transformer feature extractor successfully completes machine translation tasks, and polysemy can be resolved using the trained word vector. Bidirectional encoder representation from transformers [45,46] (BERT) extracts bidirectional text features from transformers and trains bidirectional language models using continuous bag of words (CBOW). When using the masked language model (MLM) training approach, some text entities are randomly removed before the language model is trained. As a result, the training's impact is substantially greater than with GPT.
## 4 Discussion
### Ulright vs. Reclined Posture  
Risks of injury to the skull, brain, c-spine, and l-spine were higher in the reclined posture than those in the upright posture. During the simulation, the shoulder belt was the only measure controlling head kinematics in a crash involving a reclined seatback. Additionally, due to the oblique nature of the representative crash condition identified for AEB-equipped vehicles, the head of the reclined occupant often missed the frontal airbag. The fixed location of the frontal airbag increased the distance between the occupant and the airbag in the reclined posture. This greater relative distance resulted in more uncontrolled head kinematics compared to the upright posture, leading to increased head rotation and elevated neck forces and moments. Previous epidemiological studies have shown similar trends [18]. While these studies considered both frontal and side-impact conditions, they reported odds ratios greater than one for injuries to the HFN (head, face, and neck) regions in a reclined posture. [PERSON] et al. (2008) have also demonstrated that as the seat is reclined, the mortality rate of front-seat occupants is increased [19].  
The average chest injury risk based on chest anterior-posterior compression decreased slightly by 4.2% in a reclined posture compared to that in an upright posture. Similar findings have been reported in previous studies, although degrees of reduction are different. One study showed that the reduction in chest compression amount was as much as 0.8% and 16.5% with a reclined angle using GHBMC and THUMS models, respectively [66]. Furthermore, the effect of seatback reclination on the risk of MAIS3 or higher chest injuries was not statistically significant in a field data analysis [18].  
The l-spine injury risk, which was based on axial compressive load, increased by 5.9% on average in a reclined posture compared to an upright posture. Previous studies have similarly reported an increase in lumbar compression force with increasing seatback angle [20; 21; 31]. Two studies have suggested that the axial force of the l-spine is increased by 37-44 N/\({}^{\circ}\)[20; 21]. Another study has shown that l-spine compression is increased by around 35 N/\({}^{\circ}\) with an OEM seat model for the additional 20 degrees of rotation from the upright posture [31]. Interestingly, the increase of the l-spine injury risk under a reclined seatback angle was more pronounced without AEB activation, which showed a 23.7% increase, than that with AEB activation, which showed only a 0.1% increase. One hypothesis is that the activation of the AEB might have changed the reclined occupant's posture closer to the upright posture, which could reduce the compressive loading to the l-spine. Lastly, the risk of lower extremities was insensitive to seatback angles.
# Architecting Software for the Internet of Thing Based Systems
## 3 Research Method for the Mapping Study
### Specify the Research Questions for the Mapping Study
#### 3.1.1 Type A--Demography and Classification of Research  
Research questions (RQs) are fundamental to a systematic and objective investigation of the topic under consideration in mapping studies and literature reviews. We have outlined a number of research questions and their objectives that are classified and presented below.  
**RQ-1.**_What is the frequency and types of publications in the area of software architecture solutions for Internet of Things (IoTs) based Systems?_  
Figure 4: An Overview of the Research Methodology for Conducting the Mapping Study.  
Figure 3: Comparison Overview of the Existing vs. Proposed Study on Architecting IoT Software.  
_Objective(s):_ To highlight the progress of research in terms of the number of publications over the years. Moreover, the types of publications (e.g., conference proceedings, journal article) reflect the types of research contributions on software architecture for IoT systems.  
**RQ-2**.: _What are the existing research themes to support software architecture solutions for IoT systems?_  
_Objective(s):_ To identify and classify the recurring solutions as research themes that support architectecting of IoT based software. Themes also help us to pinpoint the focus of existing and potential futuristic research.
# Microbes and Parameters Influencing Dark Fermentation for Hydrogen Production
###### Abstract  
Dark fermentation is a promising method for hydrogen (H\({}_{2}\)) production utilizing the metabolic pathways of diverse microbial communities. This process can be carried out without the need for light, making it easier and more efficient to operate in different environments and at a lower cost. It also utilizes a wide range of substrates, making it highly adaptable to waste-to-energy applications. _Clostridium_ spp. are particularly favored in this method due to their versatile metabolism, ability to utilize a wide range of substrates, and high H\({}_{2}\) yields. Anaerobicbes and facultative anaerbes are mostly used in studies due to their efficient hydrogenase enzyme activity and metabolic pathways. A pH range of 5.5-6.5 and a temperature of 30-37 \({}^{\circ}\)C for mesophiles and 55-60 \({}^{\circ}\)C for thermophiles are usually preferred in addition to the other parameters such as hydraulic retention time and substrate used. The highest H\({}_{2}\) yield of 9.39 mol H\({}_{2}\)/mol sucrose consumed was obtained by _C. beijerinkii_ using sucrose as a substrate under batch mode conditions at 37 \({}^{\circ}\)C and pH 6-7. The review analyzes different bacterial species and examines the influence of optimized parameters required on H\({}_{2}\) yield in different bioreactor operating modes.  
hydrogen production; anaerobic digestion; hydraulic retention time; metabolic pathways; organic substrates +
Footnote †: journal: Applied sciences  
1 FibEnTech-UBI, Department of Chemistry, University of Beira Interior, 6201-001 Covilha, Portugal; [PERSON]@ubipit.(S.G.); [PERSON]@ubipit (A.F.); [PERSON]@ubipit (A.L.).  
2 Departamento de Farmacologia, Fisologia y Medicina Legal y Forensere, Facultad de Veterinaria, Universidad de Zaragoza, Miguel Servet 177, 50013 Zaragoza, Spain; [PERSON]_ADDRESS]  
3 Instituto de Investigacion en Ingenieria de Aragon (I3A), Escuela de Ingenieria y Arquitectura (EINA), Departamento de Quimica Analitica, Universidad de Zaragoza, Maria de Luna 3 (Edificio Torres Quevedo), 50018 Zaragoza, Spain
## 4 Antenna Prototype Fabrication and Test Results
### Differential Feed Network  
Ideal differential feeding requires equal amplitude (i.e., 0 dB amplitude imbalance) and 180\({}^{\circ}\) phase difference at the two differential ports. This was easily realized in HFSS simulation by setting up the correct amplitude/phase for each excitation port and assigning differential pairs. In measurement, a feed network (shown in Figure 8) was constructed from an L-band (i.e., 1-2 GHz) 3 dB 180\({}^{\circ}\) hybrid coupler, phase trimmers, 50 \(\Omega\) coaxial cables, and 90\({}^{\circ}\) angled SMA adaptors.  
Figure 7: OCROP near field distribution for 1. 5 GHz at 1\(\lambda\) above the aperture opening: **(a)** Oblique view; **(b)** Bottom view.  
Since the antenna ports are in the sidewalls of the ground cavity, we used 90\({}^{\circ}\) angled SMA connectors to eliminate the stress of the cable mass on the prototype, and these were used to connect the antenna to the feed network. Without the phase trimmers, the phase difference between the signals from port 0\({}^{\circ}\) and port 180\({}^{\circ}\) of the coupler and cable combination exhibits relatively large deviations from the ideal value of 180\({}^{\circ}\).  
A vector network analyzer (i.e., VNA) was used to evaluate the S-parameters of the feed network and the antennas. The magnitude imbalance at the end of the cables was \(\leq\)0.5 dB within the 1.05 to 2.0 GHz bandwidth. After adding the phase trimmers, the differential phase error within 1.05 to 1.9 GHz bandwidth was \(\leq\)5\({}^{\circ}\). So, in measurement, the feed network with the phase trimmers was used to provide differential signals to both fabricated antennas.
# Identification of Hybrid Okra Seeds Based on Near-Infrared Hyperspectral Imaging Technology
## 2 Materials and Methods
### Near-Infrared Hyperspectral Imaging  
A laboratory-built hyperspectral imaging system was used to acquire hyperspectral images of okra seeds. The whole system includes the following equipment: an imaging spectrograph (ImSpector N17E; Spectral Imaging Ltd., Oulu, Finland); a high-performance CCD camera (C8484-05; Hamamatsu, Hamamatsu City, Japan) coupled with a camera lens (OLES22; Specim, Spectral Imaging Ltd., Oulu, Finland); two 150 W tungsten halogen lamps (Fiber-Lite DC950 Illuminator; Dolan Jenner Industries Inc., Boxborough, MA, USA); a mobile platform controlled by a stepper motor (Isuzu Optics Corp., Taiwan, China); and, a computer equipped with the data acquisition software (Xenics N17E, Isuzu Optics Corp., Taiwan, China) that controls the motor speed, exposure time, and so on. Next, a non-deformable and clear image should be obtained by the system. The spectral range of the hyperspectral imaging system whose spectral resolution is 5 nm is 874-1734 nm. The camera has 320 \(\times\) 256 (spatial \(\times\) spectral) pixels. In order to obtain clear and usable spectral images, relevant parameters of the test system need to be set before spectral collection. The height of the objective lens was set to 15 cm, the exposure time was set to 3 ms, and the moving speed of the platform was set to 15 mm/s. Before the spectral data and imaging process, the raw hyperspectral images should be corrected. The white reference image was acquired by using a white Teflon tile with nearly 100% reflectance. The black reference image was acquired by covering the lens completely with its opaque cap when the lights were all turned off. The calibrated image was calculated while using the following equation:  
\[I_{C}=\frac{I_{raw}-I_{dark}}{I_{white}-I_{dark}} \tag{1}\]  
where \(I_{raw}\) is the raw hyperspectral image; \(I_{dark}\) is the dark reference image and \(I_{white}\) is the white reference image; \(I_{C}\) is the calibrated hyperspectral image.