esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Statistical Analyses  
For each outcome variable and group, means and standard deviations (SD) were calculated. Differences between four pairs of group comparisons were undertaken: (1) Overall PFP vs. control groups were contrasted, which involved all PFP runners compared against all of the asymptomatic runners. Following this subset, analyses of (2) male PFP vs. male control, (3) female PFP vs. female control, and (4) male PFP vs. female PFP were undertaken. To compare participant characteristics (i.e., age, mass, stature, BMI, running speed, step length, mean weekly running volume), linear mixed models with group modelled as a fixed factor and random intercepts by participants were adopted in order to compare pairs 1-4; symptom duration and KOOS-PF were comparable in pair 4 only.  
To compare biomechanical outcomes across the four pairs of groups, the same approach was adopted, adjusting only for running speed, which was modelled as a continuous fixed covariate. In addition, for group comparisons from pairs 1-3 outlined above, binary logistic regression analysis was conducted with the biomechanical parameters as the predictor variables and classification into either the PFP or the control group as the outcome variable. Finally, two-way Pearson's chi-squared (X\({}^{2}\)) tests of independence with probability values calculated by Monte Carlo simulation were used to assess differences in the distribution of the three footstrike patterns between the pairs. Only parameters identified as being significantly different between control and PFP groups using linear mixed-model analyses were considered for the regression analyses. All statistical analyses were conducted using SPSS v27 (IBM, SPSS). For linear mixed models, the mean difference (\(b\)), t-value, and 95% confidence intervals of the difference are presented. For binary logistic regression analyses, the odds ratios (ORs) and 95% confidence intervals of the odds ratios are presented. Odds ratio's represented the odds of being classified into the PFP group for every 1 unit increase in the predictor variable. Statistical significance for all analyses was accepted at the \(p<0.05\) level. In the interests of conciseness and clarity, only variables that presented with statistical significance are presented in the Results section.
# A Hybrid DNN Multilayered LSTM Model for Energy Consumption Prediction
## 5 Experimental Results
### Data Preparation
#### 5.2.3 Normalization and Feature Selection  
Normalization was applied to the dataset using the MinMax scalar function, and the data were normalized such that the feature values were between 0 and 1. The features from the dataset were selected using the Pearson correlation coefficient, the values of which ranged from \(-1\) to 1. To determine correlation, covariance was applied between the two terms to show how strongly the features correlated. The voltage feature showed a negative correlation relative to the other features, as shown in Figure 4.
## 3 Survey of Techniques for Updating
### The Execution Environment
#### 3.3.22. FlexCup  
FlexCup [76] enables on-the-fly reinstallation of software components in TinyOS. In a manner similar to Dynamic TinyOS, components can be linked into separate _binary components_, which are subsequently linked at runtime into a TinyOS system. FlexCup holds the following metadata: generic program information, a program-wide symbol table, and a relocation table for the binary components. A 5-step algorithm is used for updates: storage of new metadata and code, symbol table merge, relocation table placement, reference patching, and installation of the new code followed by a reboot. FlexCup Basic transmits entire updates; FlexCup Diff only transmits incremental changes--this requires the base-station to know the exact configuration of the sensor node to prepare a diff script.  
FlexCup provides no specific features related to autonomic updates, but as for Dynamic TinyOS, it provides additional granularity and performance improvements that may be useful features in an autonomic environment.
# Cooperative Communication Based Protocols for Underwater Wireless Sensors Networks: A Review
## 6 Challenges and Future Trends in Cooperative UWSNs
### The Role of Machine Learning in Future Cooperative UWSNs Management
Firstly, it is important to verify the applicability of these mechanisms in the underwater environment, as they may require significant processing capabilities that may not be available to simple battery-powered underwater nodes. Moreover, the processing time may represent another issue. Actually, this latter is negligible in UWAC since the communication is low rate and and affected by long propagation delays. On the other hand, for high speed underwater links as those based on optical wireless communication, the impact of processing time should be carefully evaluated. Furthermore, in the field of reinforcement learning where a sort of digital twin is developed for considering and training all possible situations, characterizing all the possible parameters (wind, seatream, temperature) may be very challenging. In summary, the integration of machine learning into underwater communications holds immense potential to overcome inherent environmental challenges, leading to more reliable and efficient underwater data transmission systems. Such advancement is pivotal for scientific research, environmental monitoring, and maritime security operations. Besides, a lot of care should be devoted at the expense in terms of latency, processing times and computational cost.
## 4 Validation and Calibration
### Calibration Protocol  
First, the signal (WMS-2_f_) is maximized at a modulation index of \(m\) = 2.2 (modulation amplitude is equal to 0.23 cm\({}^{-1}\)) for 660 mbar at 0 \({}^{\circ}\)C which corresponds to the median value of the pressure between the ground (\(\sim\)1000 mbar) and 10 km altitude (\(\sim\)200 mbar). The AMULSE is then ready to enter the calibration process inside the atmospheric chamber described above. The following procedure is based on applying three consecutive purges of the enclosure using an absorption-free gas cylinder, then on injecting different pressure levels of a well-known CO\({}_{2}\) concentration. Once the temperature for each pressure level is stabilized (\(<\)0.01 \({}^{\circ}\)C), the WMS signal (2_f_/1_f_) is recorded during five minutes at a rate of 5 Hz. This procedure is repeated for four different CO\({}_{2}\) concentrations (370.72 \(\pm\) 0.02 ppm, 383.52 \(\pm\) 0.01 ppm, 424.3 \(\pm\) 0.01 ppm and 435.98 \(\pm\) 0.01 ppm) at twenty-one evenly spaced pressure levels ranging from 200 to 1200 mbar and for eleven evenly spaced temperatures ranging from \(-\)20 \({}^{\circ}\)C and \(+\)30 \({}^{\circ}\)C.  
Figure 5 shows an example of the results obtained at 15 \({}^{\circ}\)C (experimental data are represented by points) for the four different concentrations previously listed. We determine then the maximum of each of those curves in order to obtain the corresponding modulation amplitude (\(a\) = 0.232 \(\pm\) 0.009).  
Figure 4: Atmospheric enclosure schematic diagram showing the thermal insulation and liquid cooler layers as well as the different elements connected and controlled by a single computer.  
Fitting the experimental data points, using the Equation (1) described by [PERSON] (11,12) and [PERSON] (17) allows us to determine the \(i_{0}\) value (\(i_{0}\) = 0.049 \(\pm\) 0.001). The least-square is then applied by minimizing the difference between our measured \(2f/1f\) signal and the calculated one using the model described by the Equation (1) leading to absolute concentration determination.
# A Theoretical Method and Model for TBM Tunnelling Trajectory Adjustment
## 2 Method
### Judge the Current Tunnelling State  
In this paper, permitted deviation and the consistency between angles and distances are used as the two indexes to judge whether trajectory adjustment is necessary for the TBM. When the two indexes are not satisfied, trajectory adjustment is conducted by controlling  
Figure 1: Flowchart of the TBM tunnelling trajectory: (**a**) bias angle and distance; (**b**) rotated angle of the main beam used to control tunnelling trajectory.  
the thrust ratio of the four groups of thrust cylinders, which are located on the top, bottom, left, and right of the main beam. The two judging indexes are described in detail as follows.  
Index 1: Limited by the tunnelling accuracy, it is difficult to completely excavate the tunnel along the designed trajectory. A small deviation is permitted, and the permitted deviation is used to judging whether trajectory adjustment is required. Usually, there are measurement points located on the TBM shield, and the distance between the actual and designed location of the measurement points, recorded as \(d_{h}\) and \(d_{v}\), are used to characterize the deviation distance of the TBM in this paper.  
Index 2: Distance and angle are used to describe the tunnelling deviation and determine the necessity of trajectory adjustment. For example, if the measurement points are located left of the designed position, while the actual tunnelling direction is right compared with the designed route, as shown in Figure 2a, then the bias distance will decrease with the continued excavation of the TBM. On the contrary, if the measurement point is located to the right of the designated position, as shown in Figure 2b, then the bias distance will increase, and the tunnelling trajectory needs manual intervention. Overall, if the bias angle and distance consistently deviate from the desired direction, then the TBM cannot automatically return to the design route with further excavation, and manual intervention is required for trajectory adjustment.
## 4 Experimental Results and Discussion  
To evaluate the performance of the proposed ARPD-RTK/INS algorithm, experiments are conducted on both simulated and real datasets. The experiment mainly compares the accuracy of the following algorithms under different conditions:  
1. Single-epoch RTK: Only the current satellite observations are utilized to perform an integer ambiguity resolution at each epoch. The AR technique that we use here is the LAMBDA method. The detailed implementation is depicted in Figure 4.
2. The first kind of tightly coupled RTK/INS integration: Raw carrier-phase measurements, pseudorange measurements, and inertial information are fused to estimate the float ambiguities using EKF. Then, the integer solution is searched for in the ambiguity domain. The Kalman filter states contain position, velocity, attitude, the biases of inertial sensors, and float ambiguities. The detailed implementation is depicted in Figure 1. In this section, this algorithm is abbreviated as \"TC-RTK/INS (1)\".
3. The second kind of tightly coupled RTK/INS integration: Least-Squares (LS) estimation is used to obtain the float ambiguities. In order to solve this LS problem, not only pseudorange and carrier-phase measurements, but also the position provided by the INS are utilized as constraints. The detailed implementation is depicted in Figure 2. In this section, this algorithm is abbreviated as \"TC-RTK/INS (2)\".
4. The proposed ARPD-RTK/INS algorithm: Different from the above three positioning methods, the new algorithm obtains the integer ambiguity solution by searching in the position domain, which is not affected by the quality of pseudorange measurements. The detailed implementation is depicted in Figure 6.
## 4 Experiments and Results
### Comparison with Other Methods  
#### 4.2.1 In Terms of a Reconstruction Effect  
Compare our method with five other methods of image super-resolution, which include Bicubic [11], Aplus [21], SRCNN, VDSR, and DRCN. The models of these methods are trained on a 291-image data set. Table 1 lists the quantitative results under different scale factors (\(\times\)2, \(\times\)3, \(\times\)4) on the test set Set5, Set14, Urban100, and BSD100. There are two types of evaluation criteria, which are subjective evaluation and objective evaluation. Subjective standard evaluation is the most intuitive and simple method. It is mainly judged by the intuitive feelings of the human eyes. This method requires people to know some important image features in advance, and then evaluate the reconstructed image, such as whether the image details are clear. Objective standard evaluation is a method of evaluation results calculated by the correlation coefficient between the images. Typically, there are two standards for the evaluation of the effect of super-resolution image reconstruction: peak signal-to-noise ratio (PSNR) and a structural similarity index (SSIM). Generally, the larger of these two values, the better the reconstruction effect. Although PSNR is more commonly used, in some cases, it may be contrary to human subjective feelings. When the PSNR value is small, the visual effect may be better. Listed in Table 1 are the average values of PSNR and SSIM of each method on each data set. As can be seen from the table, compared with VDSR and DRCN, the PSNR and SSIM of our proposed method have advantages only on some data sets, but the differences are not large. It can be seen that the reconstruction effect of our method is almost the same as the effective method proposed previously. Since the main purpose of this paper is to reduce the computational complexity without affecting the reconstruction effect, the running time is reduced. Thus, what this section discusses is that, compared with other deep neural networks, the reconstruction effect is not affected. In addition, as can be seen from Figure 4, in the reconstruction effect of some images, the outline of our method is significantly cleaner and more vivid, which is closest to the ground truth image.
# A Survey of Security Vulnerability Analysis, Discovery, Detection, and Mitigation on IoT Devices
## 3 Vulnerability Analysis, Discovery, Detection, and Mitigation
### Research on Vulnerability Detection  
The dynamic and static analysis techniques from the previous section can also be applied to detect known vulnerabilities. In large-scale detection scenarios, the dynamic analysis relies on architecture-specific tools to execute. In addition, the static analysis method detects known vulnerabilities by way of mining zero-day, increasing performance, and time consumption. At present, researchers mainly adopt the following two methods: network scanning and code similarity detection.  
\begin{table}
\begin{tabular}{c l l l l} \hline \hline
**Category** & **Ref.** & **Target** & **Technology** & **Types of Finding Vulnerabilities** \\ \hline \multirow{8}{*}{Dynamic Analysis} & [PERSON] [52] & Smart Card & Fuzzing & Logic Vulnerability \\  & Kamel [53] & Smart Card & Fuzzing & Logic Vulnerability \\  & Kosche [54] & Smart Car & Fuzzing & Weak Access Control \\  & [PERSON] [55] & Smart Car & Fuzzing & Weak Access Control \\  & IoTFuzzer [57] & Smart Home & Fuzzing & 80, NPD \\  & Costin [58] & Router & Fuzzing & CI, XSS, CSRF, SQL Injection \\  & FirmFuzz [59] & Smart Home & Fuzzing & BO, NPD, CI, XSS \\  & Firm-AFL [60] & Smart Home & Fuzzing & BO, NPD \\  & Costin2014 [64] & Binary code & Fuzzy hash & Weak Authentication, Backdoor \\  & FIE [65] & Binary code & Symbolic execution & BO \\  & Firmalice [67] & Binary code & Symbolic execution & Backdoor \\  & SainT [68] & APP & Static Taint analysis & Data Leakage \\  & DTaint [69] & Binary code & Static Taint analysis & BO, CI \\ \hline \hline \end{tabular}
\end{table}
Table 2: This table is a summary of IoT device vulnerability discovery technology. BO = Buffer Overflow. NPD = Null Pointer Dereference. CI = Command Injection. CSRF = Cross-site Request Forgery
# Neural Machine Reading Comprehension: Methods and Trends
## 4 Datasets and Evaluation Metrics
### Datasets
#### 4.1.3 Span Extraction Datasets  
* SQuAD  
The Stanford Question-Answering Dataset (SQuAD), proposed by [PERSON] et al.[6] of Stanford University, can be regarded as a milestone of MRC. With the release of SQuAD dataset, an MRC competition based on it has drawn the attention of both academia and industry, which in turn has stimulated the development of various advanced MRC techniques. Collecting 536 articles from Wikipedia, [PERSON] et al. ask crowd-workers to pose more than 100,000 questions and select a span of arbitrary length from the given article to answer the question. SQuAD is not only large but also high quality. In contrast to prior datasets, SQuAD defines a new kind of MRC task, which does not provide answer choices, but needs a span of text as the answer rather than a word or an entity.  
* NewsQA  
NewsQA [71] is another span extraction dataset similar to SQuAD, in which questions are also human-generated and answers are spans of text from corresponding articles. The obvious difference between NewsQA and SQuAD is the source of articles. In NewsQA, articles are collected from CNN, while the SQuAD is based on Wikipedia. It is worth mentioning that some questions in NewsQA have no answer according to the given context. The addition of unanswerable questions makes it closer to reality and inspires [PERSON] et al. [72] to update SQuAD to version 2.0. In terms of unanswerable questions, we give a detailed introduction in Section 5.2.  
* TriviaQA  
The construction process of TriviaQA [73] distinguishes it from previous datasets. In prior work, crowd-workers are given articles and pose questions closely related to those articles. However, this process results in the dependence of questions and evidence to answer them. Furthermore, in human understanding, people often ask a question and then find useful resources to answer it. To overcome this shortcoming, [PERSON] et al. gather question-answer pairs from trivia and quiz-league websites. Then they search for evidence to answer questions from webpages and Wikipedia. Finally, they build more than 650,00 question-answer-evidence triples for the MRC task. This novel construction process makes TriviaQA a challenging testbed with considerable syntactic variability between questions and contexts.  
* DuoRC  
[PERSON] et al. [65] also try to reduce lexical overlap between questions and contexts in DuoRC. As in Who-did-What [64], questions and answers in DuoRC are created from two different versions of documents corresponding to the same movie, one from Wikipedia and one from IMDb. Asking questionsand labeling answers are done by different group of crowd-workers. The distinction between the two versions of movie plots requires more understanding and reasoning. Moreover, there are unanswerable questions in DuoRC.
## 2 Materials and Methods
### Presentation of the Scanning Scheme
For the fixed Cartesian coordinate system, the vectors **e\({}_{\text{y}}\)** and **e\({}_{\text{z}}\)** correspond to the rotation axis and the radial direction, respectively, when the rotation angle \(\alpha_{rot}\) equals zero. The translational axis of the scan is **e\({}_{\text{x}}\)**. For the diagrammed position, the rotation angle is \(\alpha_{rot}\) (here \(\alpha_{rot}<0\)) and the translation position relative to the origin O is \(l_{trans}\). The distance \(d_{u}\) is a distance to the transducer axis in the elevation direction used during the reconstruction process. (**c**) The transducer axis is shown for several positions of the scanning process. The translation positions are illustrated for three different rotation angles: \(\alpha_{rot}\) a, \(\alpha_{rot}\) b, and \(\alpha_{rot}\) c. For each angle \(\alpha_{rot}\), the translation ranges from \(l_{trans\_{center}}(\alpha_{rot})-\text{L}/2\) to \(l_{trans\_{center}}(\alpha_{rot})+\text{L}/2\). For two successive positions at a constant \(\alpha_{rot}\), the spacing between the transducer axes is \(\Delta l\) and the transition step is \(\Delta l/\cos(\alpha_{rot})\) along the translation axis. The magenta square indicates the imaged area, i.e., the region probed with all the different orientations of the transducer axis. This area is named DSCA for Diamond-Shaped, Cross-sectional Area.
## 4 Applicability on Performance-Constrained Platforms  
Real-world systems operate under severe resource constraints, requiring cryptographic algorithms that are not only secure against quantum threats but also computationally efficient. Speed is another common factor; whether it is real-time V2V communication or  
\begin{table}
\begin{tabular}{l c} \hline \hline  & **Publications** \\ \hline Dilithium & [40; 41; 42; 43; 44; 45; 46; 47; 48] \\ Falcon & [48] \\ Sphincs+ & [48; 49; 50; 51] \\ \hline \hline \end{tabular}
\end{table}
Table 6: Recent relevant algorithm implementations—list of publications (2022 and 2023).  
Figure 7: Key generation speeds (in milliseconds) for digital signature algorithms for the i7-6700 processor (Dilithium for NIST levels 1–3, other algorithms for levels 1–5; bars) [38] and the i7-1165G7 processor (Dilithium 3 and Falcon 512; blue and green dots) [27].  
swift financial transactions via smart cards, the selected post-quantum algorithms must offer rapid signature verification and message processing. Moreover, the transition to post-quantum secure algorithms involves a complex trade-off between security, computational load, key sizes, and signature verification times, often necessitating hardware-accelerated solutions or innovative approaches like threshold signatures.  
Also, the NIST's other recent initiative [52; 53] underscores the critical need for lightweight cryptography in constrained environments as analyzed, for example, in [54]. These efforts reflect a future trend in the cryptographic community towards developing solutions that are both quantum-resistant and resource-efficient. Namely, as quantum computing progresses, the expectation is that quantum-resistant cryptographic algorithms will also need to be lightweight to be practical for devices with limited computational capabilities.  
Thus, in our selection, we focused on systems that represent a diverse range of constraints and for which relevant data on post-quantum signature performance are available.