esa-sceva/satcom-chunk-collection huggingface dataset:
# Security Analysis of the Symmetric Cryptosystem Tiny Jambu
## 2 The TinyJambu Cryptosystem: An AEAD Scheme  
The NIST Lightweight Cryptography Standardization Project solicited cryptographic proposals with double functionality: authentication and encryption both unified in the same algorithm (AE scheme). Moreover, such algorithms should incorporate the management of associated data that are additional non-confidential information such as headers, IP directions, routing protocol, etc., used in the communication process. In brief, an AEAD scheme (authentication and encryption with associated data scheme) was required for all candidates in the NIST call [13]. At the same time, one part of the information should be ciphered (plain text or confidential message), and another, part non-ciphered (nonce and associated data), but all data must be authenticated by means of a tag constructed for verification purposes. The required minimum sizes of the algorithm parameters were as follows: a key (\(k\)) of 128 bits, a nonce (\(N\)) of 96 bits and a tag (\(T\)) of 64 bits, while the length of the plain text (\(M\)) and associated data (\(AD\)) was variable. Notice that only (\(k\)) and (\(M\)) must be kept secret; the other parameters are public.  
Figure 1 shows an exchange of information between a sender and receiver in the NIST operation mode.  
Figure 1: Operation mode required by the NIST: (**a**) steps performed in emission; (**b**) steps performed in reception.  
In emission, the sender makes use of the key, nonce, associated data and plain text to compute the ciphered text and tag. Later, they send to the receiver both computed items. In reception, the receiver makes use of the same key, nonce, associated data and the received ciphered text to compute the plain text and their own tag (denoted by Tag'). Then, they compare the tag received with the computed Tag'. If both coincide, then the plain text is accepted; otherwise, it is rejected.
## 5 Results and Discussion
### Performance Under Varying Weather Conditions  
The weather conditions significantly affected the performance of VRDeepSafety. VRFormer, operating within the platform, was tested in clear skies, fog, and rain by measuring the APA, response time, and navigation accuracy. Even when the weather limits the in  
Figure 11: Performance gains with increasing V2X intermediaries.  
Figure 10: Increased visibility accuracy comparison.  
dividual sensor performance, the VRDeepSafety system benefits from sensor fusion and V2X communication.  
Figure 12 illustrates the impact of different weather conditions on the VRDeepSafety system's performance, as measured by APA, navigation accuracy, and response time, all obtained using the VRFormer model. The VRFormer's accident prediction accuracy, measured at a 2 s prediction horizon, was highest under clear skies (90%), decreasing to 75% in foggy, and to 80% in rainy conditions, as shown in Figure 12. The navigation accuracy for VRFormer peaked at 97% under clear skies, dropping to 86% in fog, and to 88% in rain. VRFormer's response times were fastest under clear skies at 1.2 s, increasing to 1.6 s in fog and 1.5 s in rain, as shown in Figure 12. These results highlight the VRFormer model's capacity to maintain operational performance in diverse weather conditions, leveraging sensor fusion within the VRDeepSafety framework.
## 6 Discussion  
The measurement and control gate, as a new type of measurement and control equipment, calculates the flow rate through ultrasonic probes arranged in layers inside the measurement box. However, when the flow pattern is turbulent or the flow pattern is rapidly changing, the individual probe will not be able to accurately obtain the path flow velocity, and the flow velocity area method will produce large errors when used for flow  
Figure 8: Calculation results and relative errors of the PSO-BP model and the layer flow velocity in the measurement box: (**a**) flow variation and (**b**) relative error.  
calculation. Due to the lack of a professional platform for the detection and testing of measurement and control gates, few studies have been carried out to investigate the calibration method for flow in the measurement box of the measurement and control gate [22; 23]. It is very important to calibrate the accuracy of the path flow velocity measured by the probe inside the measurement box during the development and debugging process of the measurement and control gate. The research in this paper indicates that using neural network models to predict flow and thus calibrate the path flow velocity inside the measurement box is a feasible solution.  
Neural network models have been widely used in radial gates and slice gates in channels [24; 25], but no studies have reported the application of neural network models in measurement and control gates. Since the BP, GA-BP, and PSO-BP models are more widely used neural network models, these three models were chosen in this study to carry out an exploratory preliminary study, and other neural network models will be chosen in the subsequent study for the application of measurement and control gates. The robustness and reliability of the optimized neural network prediction models are assured by comparing them with a large amount of measured data. Both PSO and GA algorithms have good applicability and acceptable computational efficiency. However, the parameter settings of the two algorithms have a significant impact on the optimization effect. The parameters used in this article are all conventional settings, which have shown good performance in other fields, but there is still significant room for improvement in gate flow prediction.  
The neural network prediction model established in this paper also has some shortcomings. The flow measurement tests were conducted under undisturbed conditions, without considering working conditions such as obstruction, disturbance in front of the gate, and the addition of diversion channels. In the later stage, flow measurement experiments will be conducted under the influence of different external factors. The increases in the input variables of the neural network can improve the generalization ability, prediction accuracy, and applicability of the prediction model. Further efforts are also needed on the system reliability evaluations of measurement and control gates produced by different manufacturers, which can more realistically evaluate the real situation in the management of agricultural irrigation.
# Passive UHF RFID Tag for Multispectral Assessment
## 3 Results and Discussion
### Tag Performance: Spectral Response  
Figure 6 shows the final step of an iterative process where spectral fingerprint coordinates and the full spectral response are fitted. Error bars come from the standard deviations calculated in the final step of the methodology explained in Section 2.3.  
Figure 5: Simulated and measured differential S-parameter of the UHF dipole antenna showing minimal return loss at 868 MHz.  
Figure 6: Comparison of the normalized spectral responses measured with the spectrometer and the spectral fingerprints obtained with the RFID tag for different types of illuminants: cool white LED, ultraviolet lamp, tungsten halogen light, and daylight. Lines are spectrophotometer outputs and dots are spectral fingerprint coordinates.  
Four illuminants are placed directly facing the photodiodes in the tag for this figure. The resulting procedure for computing the spectral fingerprint is an estimation based on the spectral sensitivities provided by each photodiode manufacturer. Despite this, the normalized experimental results can be fitted to the spectral emissions of each illuminant. Therefore, after this optimization process and accounting for the normalization factor correction, the spectral fingerprint obtained allows the identification of the illuminant source applied to the tag.
## 5 Conclusions  
In order to achieve interference suppression and DOA estimation, a blind adaptive beamforming algorithm for a GNSS array receiver based on DiLL is proposed. Firstly, the subspace projection principle is used to achieve interference suppression, and the direction lock loop is used to achieve high-precision tracking of the DOA of navigation signals in the correlation domain. Then, the beamformer is constructed with the estimated DOA estimation results, which can provide gain by forming a beam in the satellite direction. After the array signals are weighted, interference suppression and navigation signal enhancement can be realized. The proposed method can realize DOA estimation in the presence of interference with low computational complexity, and thus achieve stable DOA tracking in dynamic scenes. It deserves great attention in the field of GNSS array antenna receivers.  
[PERSON] performed the theoretical study, conducted the experiments, processed the data, and wrote the manuscript. [PERSON] and [PERSON] provided research suggestions and revised the manuscript. [PERSON] helped in performing the experiments. [PERSON] provided the experimental equipment and suggestions for the manuscript. All authors have read and agreed to the published version of the manuscript.  
This research was supported in part by the Natural Science Foundation of China (NSFC) grants U20A0193.  
No date supporting.  
The authors would like to thank the editors and reviewers for their efforts towards the publication of this paper.  
The authors declare no conflict of interest.
## 5 Result Analysis
### Identity Authentication Security Performance Analysis
System throughput and delay analysis  
In this study, transaction throughput (the number of transactions successfully delivered to the blockchain per second) and latency (the time difference between transaction delivery and response reception) are first tested. Two typical function-calling smart contract transactions are tested: (1) Insert, which changes the state of the blockchain and needs to be sent to peer nodes of other organizations for verification; (2) Verify, which can be directly executed. As demonstrated in Figure 13, the throughput of the Verify function is proportional to the block size, consistent with the theory. When the number of nodes is set to three, the transaction size in the block is set to 4 KB, and the block generation time is set to 1 s. The relationship between the blockchain data block size and throughput is not a straight line. Nonetheless, the slope of the curve drops sharply when the block size is about 1500 KB. This is because the size of the network packet is set at about 1500 KB in the P2P network of the blockchain, requiring it to send the original block in packets after the block size exceeds  
Figure 12: Authentication efficiency of different Hash algorithms. 1500 KB. As a result, the transmission delay in the network presents a square increase, and the increase in data throughput decreases. When the block size is greater than 1500 KB, the transaction throughput using Insert increases slowly with the increase in the block size. The throughput is stable at 350 tps when the block size exceeds 1500 KB. However, the transaction throughput using Verify continues to increase as the block size increases. Figure 14 unveils that the transaction latency using Verify does not change with the increase in the block size and remains around 10 ms, since such transactions should not be verified by other nodes. The increasing rate of transaction latency using Insert suddenly increases when the block size exceeds 1500 KB. Thus, if the block size is set to 1500 KB, the latency is about 1 s, and the throughput is 300 tps. The system using Insert to write data in the blockchain during the initialization process should adopt Verify to verify the information during the verification process. Figures 13 and 14 suggest that the size of the block can be controlled at 1500 KB. At this time, the system can maintain low latency while obtaining a large throughput during the  
Figure 14: The relationship between data delay and data block size. Figure 13: The relationship between data throughput and data block size. initialization process and keep a good throughput during the verification process, satisfying the design requirements of the system.
## 2 Experimental Setup and Methods  
For LIBS analysis, the laser source which we used in this experimental arrangement is a Nd: YAG laser source ((Quantel-Brilliant-B), France) second-harmonic (2\(uv\)), having a repetition-frequency of 10 Hz, 532 nm optical-wavelength, pulse-duration of 5 ns, and provides pulse energy around 200 mJ [38; 39]. The experimental arrangements of the LIBS setup were conducted at room-temperature. The laser produces a beam which was focused onto the target surface using a convex-lens (quartz) having a 20 cm focal-length. The target samples were kept on a rotating-stage to provide a fresh sample surface for each laser pulse shot. An optical-emission-spectra were captured through an optical-fiber connected to a charge-coupled devices (CCDs) array spectrometer having a wavelength range varies from 200 to 720 nm [40; 41]. Besides the target sample was cleaned with the help of an average of five to seven laser shots on the sample surface.  
For EDX measurement, an Oxford-Instruments X-MAX-N-20 EDX-instrument in combination with a scanning-electron-microscope (SEM) machine capable of running at \(\sim\)30 keV threshold energy was used to estimate chemical-composition of all the seed samples. Similarly, an X-ray fluorescence JSX-3202-M system functioned at 5 to 50 kV varies from 0.01-1.0 mA with a high optical resolution of 150 eV at 6.0 keV was used in this study for the measurement of elemental analysis of seed samples.  
Table 1 summarizes the chemical composition (\(\mu\)g/g) of zinc in various _P. harmala_ seed samples along with the standard deviation (SD) within the same sample. To study the samples by LIBS, the samples were cleaned in an ultrasonic-bath by wetting them for 50 min in acetone. Furthermore, the sample was dried for 80 min at 100 \({}^{\circ}\)C in an oven to remove moisture. It was then finely ground to achieve homogeneity. The pellet of about 10-mm in diameter as well as 5 mm thick was prepared as a raw sample by exerting about 7 tons of pressure. For the discrimination analysis, several samples (\(\alpha\), \(\beta\), \(\gamma\), and \(\delta\)) were prepared by adding different compositions (\(\mu\)g/g) of zinc in the as-prepared raw powder of _P. harmala_ seeds.
## 4 Discussion
### eGenes for Lysosomal Pathway  
The eGenes shared by skeletal muscle and pancreas exhibited the most significant enrichment in the analysis (_p_ = 8.4 \(\times\) 10\({}^{-5}\)). These eGenes encode NPC1, IDUA, HYAL3, GBA, CTSH, AP3S2, ABCB9, and AP3B2. There is suspicion of potential pathogenic mechanisms related to lysosomal dysfunction that link some of these eGenes to T2 DM. For instance, NPC1, which is located in the lysosomal membrane, is responsible for transporting low-density lipoproteins from the late lysosomal interior to the cytoplasm. Mutations in this gene, such as rs80358259 (Ile1061 Thr), lead to Niemann-Pick type C disease, a neurodegenerative lysosomal disorder characterized by disrupted lipid metabolism and the accumulation of cholesterol and glycosphingolipids in the lysosomes of the brain, lungs, liver, and spleen [25]. Moreover, common exonic variants in NPC1, rs1805082 (Ile85 Val) and rs1788799 (Met6421 le), which encode its major structural domains, have shown associations with susceptibility to T2 DM as well as obesity (_p_ < 0.05) [10]. GBA, another lysosomal membrane protein, cleaves the beta-glucosidase linkage of glycosceramide. Mutations in this gene can cause [PERSON] disease, characterized by the accumulation of gluccorebrosides in lysosomes [26]. CTSH is a cysteine cathepsin that plays a critical role in the degradation of lysosomal proteins. IDUA and HYAL3 are involved in the lysosomal degradation of glycosaminoglycans. However, the functions of AP3S2, ABCB9, and AP3B2 have not been well determined.
# Context-Aware Complex Human Activity Recognition Using Hybrid Deep Learning Models
## 4 Experiments and Results
### Experiments and Performance Evaluation  
We conducted the experiments using the Google Colab environment, an open-source python-based machine learning platform that leverages the power of Google hardware, including GPUs and TPUs. Google Colab is known for its tremendous support for training neural networks. Moreover, it requires zero configuration, free access to GPUs, and is much faster than any other easily accessible hardware. This environment also provides leverage on the open-source interfaces, such as the TensorFlow machine learning framework, which comes bundled with Keras high-level API [74].  
In this article, two different models were built, one from the _noncontextual_ experiment and the other from the _contextual_ experiment. We refer to the _noncontextual_ model as the baseline model. The baseline model was trained using the inertial signals only, while the _contextual_ model was trained on both the inertial and ambient signals. We compared the results from the two different models using the F-measure. We trained the models on the training data and validated the model with the validation dataset. Class prediction using test data that are totally new to the model is the approach adopted to test the performance of the final model. In a bid to ensure that all the different activity classes were well-represented in each of the sets, we split the custom dataset into train and test sets using the ratio 85:15. A further split on the train set to create train and validation sets was at the ratio 80:20, giving us a 68:17:15 split among the train, validation, and test sets. Figure 6 illustrates the splits showing training (blue signals), validation (orange signals), and test (green) samples of the accelerometer x-axis for _Ascending Stairs_ activity.
## 2 Materials and Methods
### Selection of Sample Case and Image Segmentation  
The anonymised chest CT dataset in the format of Digital Imaging and Communications in Medicine (DICOM) of a 40-year-old male patient, from a previous study [25] (HRE2018-0087), was selected as the sample case for the 3D-printed model due to the presence of multiple lung nodules (31 nodules). The patient had a weight of 64 kg and a height of 170 cm. The CT scan was performed on a 640-slice CT scanner (uCT 960+ CT, United Imaging Healthcare, Shanghai, China) using 120 kVp, 125 mAs, and 1.0 mm slice thickness.  
The CT dataset was imported into 3D Slicer (Version 5.6.1, MA, USA) and segmented using manual techniques by harnessing thresholding for the skin, subcutaneous fat, muscle, diaphragm, lung nodules, and pulmonary arteries. TotalSegmentator [27], a 3D slicer extension tool, was used to automatically segment the lungs, heart, vessels, trachea, oesophagus, thyroid, bones, stomach, spleen, pancreas, and liver. Furthermore, the lungs, oesophagus, and stomach were manually hollowed using SegmentEditorExtraEffects [28]. The 3D volume rendered (VR) model (Figure 1) was then converted into stereolithography (STL) files.
# Sentiment Analysis of Online New Energy Vehicle Reviews
## 5 Experimental Results
### Precision Experimental Results Comparison  
The objective of sentiment analysis is to identify the sentiment polarity expressed in text, which is typically categorized into positive, negative, and neutral classes. Sentiment analysis has extensive applications in various natural language processing tasks. Precision measures the accuracy of the model in predicting positive sentiment, specifically the ratio of correctly predicted positive sentiment samples to all samples predicted as positive sentiment.  
Figure 10 illustrates a comparative analysis of the precision experimental results between the EDC model and models employing the word2 vec and BERT pretraining methods.  
Based on the precision results depicted in the graph, the EDC model demonstrates superior performance compared to the models using word2 vec as the pretraining method. During the initial epoch (Epoch 1), the EDC model achieves a precision of 97.19%, slightly outperforming the other models. As the epochs progress, the EDC model exhibits a consistent improvement in precision and maintains a high level of precision (exceeding 97%) in subsequent epochs, showcasing a stable performance. The DPCNN model closely trails the EDC model in terms of precision, gradually enhancing its precision in later epochs, albeit peaking at 94.3%. On the other hand, the TextCNN, TextRCNN, TextRNN_Att, and Transformer models demonstrate commendable precision, but their highest precision values hover around 90%, placing them slightly behind the EDC model.  
In comparison to models utilizing BERT as the pretraining method, the BERT_CNN, BERT_RNN, BERT_RCNN, and BERT_DPCNN models achieve precision levels of approximately 94% across different epochs, displaying substantial fluctuations. The BERT_RNN model exhibits a relatively stable performance and approaches the EDC model's performance in later epochs, albeit slightly lower. Notably, the EDC model attains a precision of 97.19% in the first epoch, surpassing the initial precision of the other models. Moreover,the precision of the EDC model remains consistently high (around 97%) with minimal fluctuation, whereas the precision of other models exhibits larger fluctuations without a stable trend. Consequently, based on the precision analysis, the EDC model holds a distinct advantage compared to other models.
## 3 Results
### Spatiotemporal Variation Characteristics of Vegetation in Xinjiang
#### 3.2.2 Analysis of Multi-Year Trend Change of Vegetation Based on Pixel  
The trend changes and spatial distribution of the annual mean NDVI and SIF provide an overall view of the vegetation conditions in Xinjiang. To gain deeper insight, a long-term trend analysis was conducted on the monthly NDVI and SIF data from 2000 to 2020 at the pixel level by calculating the correlation coefficient for each pixel. Figure 7 illustrates the distribution of the correlation coefficients for the NDVI and SIF (with numerous invalid SIF values, mainly in the desert areas of southern Xinjiang). The results showed that 92.65% of the NDVI correlation coefficients were positive, indicating an upward trend in most areas across Xinjiang, which is consistent with the inter-annual variation in the annual mean NDVI. The regions with negative NDVI correlation coefficients were primarily located in northern Xinjiang, aligning with the downward trend observed in the SPEI of the ecological region. For SIF, 74.66% of the correlation coefficient regions were positive, whereas negative regions were more pronounced in the R3 ecological zone, further supporting the observation that SIF was more sensitive to drought than NDVI. The decline in SIF within R3, in contrast to the overall upward trend in the study area, suggests that areas with high vegetation coverage in Xinjiang are more severely affected by drought, whereas drought conditions in the overall region have shown relative alleviation.