esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
To address the high complexity of existing interference detection algorithms in satellite communication systems, this paper proposes an efficient differential amplitude interference detection algorithm based on non-uniform adaptive segmentation. Simulation experiments on different types of interference signals demonstrate that, compared to the classical CME and FCME algorithms, the proposed algorithm offers superior overall performance under wideband interference conditions. In the future, we will further optimize the algorithm and explore its hardware implementation on FPGA chips to enable real-time interference detection in practical satellite communication environments, thereby enhancing system anti-interference capabilities and engineering application value.  
Conceptualization, K.C. and L.Y.; methodology, K.C. and T.G.; software, K.C.; validation, K.C., L.Y. and T.G.; writing--original draft preparation, K.C.; writing--review and editing, K.C., L.Y. and T.G.; supervision, L.Y., Y.W. and L.L. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The data presented in this study are private, from the School of Electronic and Information Engineering, Nanjing University of Information Science and Technology, and The Sixty-third Research Institute, National University of Defense Technology.  
The authors declare no conflicts of interest.
# Mesh Stiffness and Dynamic Modeling and Analysis of Modified Straight Bevel Gears
## 2 Time-Varying Meshing Stiffness Calculation Model
### Tooth Profile Modification  
The TPM is shown in Figure 5. The change in meshing stiffness of the TPM only changes in the double-tooth zone and does not change in the single-tooth zone. \(E_{kj}\) represents the TPM amount in the double-tooth area of the main and driven gears, respectively. The tooth profile modification formula is expressed as [34]:  
\[\begin{split} E_{kj}(\max)=E_{k1}(\max)R_{i}/R\\ E_{kj}=E_{kj}(\max)x\epsilon_{j}/Le_{j}^{1.5}\end{split} \tag{26}\]  
As depicted in Figure 5, the TPM is positioned following design requirements within the second double-tooth contact region. The change in meshing stiffness is limited to this double-tooth contact region, with no alterations occurring in the single-tooth contact region. The symbols \(\sigma_{1}\) represent the elastic deformations of tooth pairs within the double-tooth contact region under loading conditions. \(E_{k}\), respectively, represents the TPM amounts within the double-tooth contact region for both the driving and driven gears.  
According to the principle of deformation coordination, the relative comprehensive error between two adjacent meshing tooth pairs can be obtained as follows:  
\[E_{t}=\sigma_{1}-\sigma_{2}=E_{kj}^{p2}+E_{kj}^{g2}-E_{kj}^{p1}-E_{kj}^{g1} \tag{27}\]  
In the formula, \(E_{kj}^{il}\) represents the amount of modification for each meshing tooth pair at every slice position on the pinion and the gear.  
The meshing stiffness of the TPM gear at this time is [35]:  
\[\left\{\begin{array}{l}K_{t}=\frac{K_{0}+K_{0}}{1+K_{0}E_{t}/T},\ \rho_{1}-\rho_{2}=E_{t}>0;\\ K_{t}=\frac{K_{0}+K_{0}}{1-K_{0}E_{t}/T},\ \rho_{1}-\rho_{2}=E_{t}<0;\end{array}\right. \tag{28}\]  
Due to the TPM involving cutting at the tooth apex, what was originally a double-tooth contact region has now become a single-tooth contact region. Therefore, the meshing stiffness for the single-tooth contact region should be expressed as:  
\[K_{e}=\max(K_{e1},K_{e2},K_{t}) \tag{29}\]  
The TPM alters the gear mesh overlap. After calculating the overlap following profile modification using TCA, the time-varying meshing stiffness of gears during single and double-tooth alternation is computed according to Equation (15).
# Measurements and Modeling of the Nonlinear Behavior of a Guitar Pickup at Low Frequencies +
## 7 Discussion  
The results presented in this paper show that a simple Hammerstein model seems to be sufficient for the pickup modeling and that using a Generalized Hammerstein model is not necessary. However, several hypotheses have been put forward, simplifying the problem that might be at the origin of small differences between the measured and modeled pickup outputs compared in Figures 10 and 11.  
First, the frequency range of the excitation signal is limited to 15-500 Hz due to the capacities of the shaker. Using a larger frequency range might have been beneficial. The nonlinearities of the pickup may differ at higher frequencies, and thus, in such a case, a complete Hammerstein Generalized model might be useful. A supplementary study would be necessary to draw a meaningful conclusion.  
Next, in the first experimental setup used for the model identification, the movement of the rigid string attached to the shaker exhibits only \(z\)-axis polarization, whereas it is known that the string being played by a guitar player exhibits rather an ellipsoid type motion in both \(y\)- and \(z\)-directions [42; 43].  
Figure 11: Recorded and synthesized signals of the voltage from the pickup.  
Figure 10: Recorded signals of the vibrating string.  
A hammer-like impact, used in the second experiment for the synthesis, has been used to excite the string in order to approach the \(z\)-axis motion of the string in the comparative measurements whose results are provided in Figures 10 and 11. Note that, even if the real movement of the string is important in both \(y\)- and \(z\)-directions [44; 45], and it has been shown that the pickup behavior is mainly influenced by the \(z\)-axis contribution [8; 40]. Moreover, the piece of the rigid string attached to the shaker is of finite length and does not exhibit any deformation compared to a string attached on the guitar.  
Even though these phenomena have been neglected, the results presented in this paper show a very good agreement between the output predicted by the model and the output obtained from the experimental measurement.
# Optical Turbulence Profile Forecasting and Verification in the Offshore Atmospheric Boundary Layer
## 2 Optical Turbulence Forecasting
### Method of Statistical Analysis  
In this paper, two statistics, namely, the bias, and root mean square error (RMSE) operators, are applied to evaluate the performance and reliability of the BPNN approach as well as different models in reconstructing optical turbulence strength [39]. The bias contains information on systematic model errors, and the RMSE contains information on the statistical errors plus the systematic errors:  
\[BIAS=\sum_{i=1}^{N}\frac{\left(Y_{i}-X_{i}\right)}{N} \tag{18}\]  
\[RMSE=\sqrt{\sum_{i=1}^{N}\frac{\left(Y_{i}-X_{i}\right)^{2}}{N}} \tag{19}\]  
where \(X_{i}\) is the individual measured values of lg\(C_{n}^{2}\), \(Y_{i}\) is the corresponding estimated values of lg\(C_{n}^{2}\) by the neural network and model, and \(N\) is the number of samples for a couple \((X_{i},Y_{i})\). Furthermore, utilizing the bias and the RMSE, we retrieve the center root mean square error (CRMSE), which represents the intrinsic uncertainty not affected by the bias, and it provides fundamental information on the systematic bias and statistical uncertainties. Taking into account the actual value of \(C_{n}^{2}\), basically in the range of \(10^{-18}\)-\(10^{-13}\) m\({}^{-2/3}\), using lg\(C_{n}^{2}\) instead of \(C_{n}^{2}\) makes it convenient for the calculation and visualization of statistical results, which then makes the statistical results more comprehensible. This is simply a substituted conversion to represent the error of \(C_{n}^{2}\), so it is reliable and valid:  
\[CRMSE=\sqrt{\sum_{i=1}^{N}\frac{\left[\left(X_{i}-\overline{X_{i}}\right)-\left( Y_{i}-\overline{Y_{i}}\right)\right]^{2}}{N}}{N}=\sqrt{RMSE^{2}-BIAS^{2}} \tag{20}\]
## 2 Materials and Methods
### Data Classification
#### 2.4.1 Image Segmentation  
In ArcGIS Pro, the \"default\" segmentation parameters are 15.5 (spectral), 15 (spatial), and 20 (min. segment size). Segmentation requires user-set parameters for spectral, spatial, and minimum segment size sensitivities, which are typically optimized through trial-and-error and vary based on pixel resolution [24]. For the first test, the post-burn dataset from Doak was segmented using these default parameters, and then adjusted until the image objects were comprised of as few segments as possible while simultaneously avoiding grouping multiple objects into one segment [37]. This was done by increasing the spectral detail, decreasing the spatial detail, and increasing the minimum segment size. Figure 7 shows the progression of segmentation parameters tested, using the Doak post-burn dataset as an example.  
Figure 7: Segmented image parameters tested. (**a**) Original image; (**b**) “default” segmentation parameters—15.5 spectral, 15 spatial, 20 minimum segment size (MSS); (**c**) 17 spectral, 10 spatial, 20 MSS; (**d**) 17 spectral, 10 spatial, 80 MSS.  
Upon optimizing the segmentation parameters, orthomosaics for the PWA and remaining Volz sites were segmented using the \"Segment Mean Shift\" tool. The parameters used were 17 spectral detail, 10 spatial detail, and 80 minimum segment size. After this was completed, the next step was to define a classification schema for each type of NDBM treatment.
## Results
### Complications  
There were a total of 50 reported complications out of 173 surgical procedures, indicating a 28.9% total complication rate. The most common complication was non-union (\(n\) = 18 cases), representing 36.0% of complications with a total complication rate of 10.4%. Other reported complications for first MTP arthrodeiss salvage include neurom (\(n\) = 3, 1.73% rate), superficial wound infection (\(n\) = 3, 1.73% rate), painless osteoarthritis (\(n\) = 1, 0.58% rate), stress fracture (\(n\) = 1, 0.58% rate), hardware removal (\(n\) = 4, 2.3% rate), deep infection (\(n\) = 1, 0.58% rate), numbness (\(n\) = 1, 0.58% rate), prominent plate (\(n\) = 2, 1.2% rate), graft site irritation (\(n\) = 1, 0.58% rate), and primary complaints of pain and/or discomfort (\(n\) = 15, 8.7% rate). Non-union rates varied from 5% to 25% for the included articles. Individual articles were assessed for complications and recorded for this systematic review for further examination (Table 5).  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Author (Year)** & **Procedures (\(n\))** & **AOFAS Pre** & **AOFAS Post (Max 95)** & **Success Rate** \\ \hline [PERSON] (2021) [21] & 38 & 43.5 & 77.2 & 94.70\% \\ [PERSON] (1997) [22] & 16 & - & - & 80\% for tricritical graft \\ [PERSON] (2017) [23] & 12 & 50.7 (range: 38-75) & 73.8 (range: 60-90) & 75\% \\ [PERSON] (2015) [24] & 11 & 44.50 (range: 32-60) & 87.75 (range: 76-96) & 90.90\% \\ [PERSON] (2015) [25] & 15 & - & - & 87\% \\ [PERSON] (2015) [26] & 25 & - & - & 88\% \\ [PERSON] (2000) [27] & 24 & 40 (range: 22–60) & 79 (range: 66–90) & 79\% \\ [PERSON] (2018) [28] & 8 & - & 87.50\% \\ [PERSON] (2013) [3] & 12 & 51.7 \(\pm\) 13.1 & 74.9 \(\pm\) 8.6 & 41.70\% \\ [PERSON] (2000) [29] & 12 & - & 70 (range: 52–82) & 91.70\% \\ \hline \end{tabular}
\end{table}
Table 3: Clinical and radiographic outcomes for the individual articles in this systematic review. Data recorded includes first author, year of publication, number of procedures, preoperative and postoperative American Orthopedic Foot and Ankle Society (AOFAS) score, and success rate.  
For subgroup analysis, the foot and ankle autograft group (\(n=12\) procedures) had an overall complication rate of 25.0% (\(n=3\) complications) with three cases of non-union (25.0% complication rate). The non-foot and ankle autograft group (\(n=45\) procedures) had an overall complication rate of 53.3% (\(n=24\) complications) with four cases of non-union (8.9% rate), two cases of neuroma (4.4% rate), one case of graft site pain (2.2% rate), four cases of hardware removal (8.9% rate), 12 cases of primary pain and/or discomfort (26.6% rate), and one case of numbness (2.2% rate). The allograft group (\(n=49\) procedures) had an overall complication rate of 10.2% (\(n=5\) complications) with one case of primary pain and/or discomfort (2.0%), three cases of non-union (6.1%), and one case of superficial wound infection (2.0%). Overall, the allograft group had the lowest overall complication rate (10.2% versus 25.0% and 53.3%). All other complications (\(n=9\) complications) were from articles that did not distinguish complications by graft type.
# System-Level Statistical Eye Diagram for Signal Integrity
## 2 Statistical Eye Diagram
### Scrambling  
Scrambling is an electromagnetic interference (EMI) suppression technique in electromagnetic compatibility (EMC) [37]. When periodic data patterns are transmitted over a channel, then the energy at a certain frequency is concentrated and starts to radiate. As a result, the radiated energy affects other electronic devices in the form of noise. The scrambling is to mix the data with the linear feedback shift register (LFSR) [38]. The LFSR is a mathematical field with a primitive polynomial and a feedback path. Because the LFSR generates a pseudo-random binary sequence (PRBS), the LFSR has been widely used as a random source. The generated random sequences are not purely random. It is not guaranteed that they will have a probability of 0.5. Therefore, a statistical eye diagram for scrambling has a different profile in terms of probability compared to the non-scrambling data. Table 1 shows how the bit probabilities change when scrambling is applied [39]. The  
Figure 9: Statistical eye diagram for the multi-level signaling [36]. The statistical eye diagrams have different PDFs on the logic level and the pulse levels. (**a**) When all of the logic levels have the same probability and the scaling factor, the statistical eye diagram is symmetric in terms of the probability and the distribution. (**b**) The asymmetry on the probability causes the asymmetric statistical eye diagram. (**c**) The different scaling on the pulse response also leads to the asymmetric PDF.  
data have different probabilities for each bit index. After scrambling, the data have almost the same probability as the bit index. In other words, the periodic pattern can be removed by scrambling to make the bit probabilities the same.  
The scrambled data bits have different probabilities for the purpose of EMI suppression. The proposed method in [39] reflects the effect of scrambling by counting the number of ONEs depending on if scrambling was used. For this, the data bits are divided in accordance with the number of registers in the LFSR. The process of splitting is equivalent to symbolizing the given bits. As a result of symbolizing, the bit index can be defined as shown in Table 1. Then, the probability of each bit index was determined to be the input for the statistical approach. Therefore, the statistical approach has the modified bit probabilities as the input.  
LFSR \(2^{7}-\)Figure 10 compares the statistical eye diagrams depending on the scrambling [39]. The principle of scrambling is complicated. However, the analysis on the eye diagram is simple. The probability distribution becomes equally distributed by the scrambling because the statistical eye diagram only shows the result in terms of the bit probability and the channel responses.
## 3 Results
### Performance of Models Using BERT-Bfd and Other Deep Representation Learning Features  
Next, we run the three other deep representation learning models, UniRep, SSA, and W2V, using all seven classifiers, to extract features a.nd compare their performance with BERT-bfd. The results of these comparisons are summarized in Figure 3. The data clearly indicated that BERT-bfd outperformed UniRep, SSA, and W2V in feature extraction, irrespective of what classifier was used for training and prediction. The best accuracy achievable with BERT-bfd was 0.9513, compared to 0.7673, 0.8611, and 0.8107 achieved with UniRep, SSA, and W2V, respectively. Furthermore, the lowest accuracy of models using BERT-bfd was over 0.92, while the ACC value of the next best option, SSA with the LGBM classifier, was 0.9021. BERT-bfd appeared to be the most suitable model for the prediction and classification of thermophilic proteins. Additionally, the best model is the one with BERT-bfd feature, which is superior to others, which may be because BERT-bfd is more novel and more efficient at capturing contextual information stored in protein sequences [32].  
Figure 3: Performance of models using BERT-bfd and other deep representation learning features, UniRep, SSA, and W2V, with seven different classifiers. (**A**) is the histogram of ACC. (**B**) is the histogram of Sn. (**C**) is the histogram of AUC. (**D**) is the histogram of Sp. (**E**) is the histogram of MCC. (**F**) is the histogram of average performance of seven different classifiers.
## 3 System Architecture
### Channel Coding  
In many digital communication systems, channel coding is a critical component. It is also known as a forward error control approach in digital systems for detecting and reducing bit errors. Channel coding can be employed on both the transmitter and receiver sides to increase system reliability. Channel coding is deployed on the transmitter side to encode raw data by adding additional bits prior to modulation. On the receiver side, however, channel coding is employed to decode data. The bit error can easily be discovered and fixed using channel coding. The relevant channel coding was used in our experiment, based on the length of the MIMO C-OOK sub-packet.  
Figure 3: The deep learning neural network model for preamble detection.  
Figure 2: The C-OOK signal with: (**a**) High SNR value and (**b**) Low SNR value.
## 5 Experimental Validation of Quadrotors
### Robot and Target Dynamics Model  
For target monitoring, each quadrotor is controlled to follow a plenary circular trajectory in 3D space. The state \(\mathbf{x}_{i}\) includes the position in 3D space and the heading angle, namely, \(\mathbf{x}_{t}=[x_{i},y_{i},z_{i},\theta_{i}]^{\top}\). \(\mathbf{u}_{i}\triangleq[v_{i},\omega_{i}]^{\top}\) denotes the control input command, namely, the linear plenary velocity and angular velocity. The actual velocity and angular velocity are contaminated by zero-mean Gaussian noises, \(w_{i,k}^{v}\sim\mathcal{N}\left(0,Q_{i}^{v}\right)\), \(w_{i,k}^{\omega}\sim\mathcal{N}\left(0,Q_{i}^{\omega}\right)\). An extra altitude noise \(w_{i,k}^{z}\sim\mathcal{N}\left(0,Q_{i}^{z}\right)\) is added to the process noise. The overall process noise covariance is denoted as \(\mathbf{Q}_{i}=\text{blkdiag}(Q_{i}^{v},Q_{i}^{\omega},Q_{i}^{z})\). On the basis of the above definition, the process model for each robot \(f_{i}\) is defined as follows:  
\[\mathbf{x}_{i,k+1}\triangleq\begin{bmatrix}x_{i,k+1}\\ y_{i,k+1}\\ z_{i,k+1}\\ \theta_{i,k+1}\end{bmatrix}=\begin{bmatrix}x_{i,k}+\Delta_{T}(v_{i}+w_{i,k}^{v })\cos(\theta_{i,k})\\ y_{i,k}+\Delta_{T}(v_{i}+w_{i,k}^{v})\sin(\theta_{i,k})\\ z_{c}+w_{i,k}^{z}\\ \theta_{i,k}+\Delta_{T}(\omega_{i}+w_{i,k}^{\omega})\end{bmatrix} \tag{33}\]  
The target ground robot is modeled with a unicycle model \(f_{i}\) similar to Equation (33). Correspondingly, the state, control input, and process noise are denoted as \(\mathbf{x}_{t}\), \(\mathbf{u}_{t}\), and \(\mathbf{Q}_{t}=\text{blkdiag}(Q_{t}^{v},Q_{t}^{\omega},Q_{t}^{z})\), respectively.
# Dynamic Personalized Recipe Recommendations Based on Facial Health Recognition
## 3 Research Methods
### Recipe Recommendations Based on Facial Health  
We use a hybrid recommendation algorithm based on content and collaborative filtering to implement recipe recommendations and design a recommendation model based on Pearson and the latent factor model (LFM) for recommendation.  
First, we identify the user's facial health status, analyze it, and update the health information in the user database. Then, we determine whether the user appears again in the previous records or not. For new users, we adopt a content-based recommendation algorithm that models items and users using the TF-IDF method. We also solve the cold start problem and use Pearson similarity to recommend TOP-N recipes. For non-new users, we use the collaborative filtering recommendation algorithm based on LFM to recommend recipes. The flow chart is shown in Figure 6.  
Finally, in recipe recommendation, we introduce a bias parameter called \"suitability factor\" to adjust the recommendation results. Due to changes in facial health status, we adjust the focus of the recipes, including efficacy and taste, according to the user's health status. For example, when a user's health condition is poor, we will recommend recipes that focus more on efficacy. For other users, we may pay more attention to functional recipes that meet their taste preferences. This kind of personalized adjustment can meet the needs of different user groups. The pseudocode of the recipe recommendation framework based on facial health is shown in below Algorithm 1.  
Figure 6: Hybrid recommendation algorithm flow chart.  
```
Input: FH = User's recent facial healthy condition;  HR = healthy recipes for Recipe1M+;  UserPreference = parameters calculated from User's historical data;  User = New User OR Old User Output: FR = Top-N recommended recipes
1 FunctionHFRR(FH,HR,UserPreference): // Get Adequcy Factors
2 AdequcyFactor = GetAdequcyFactors(FH,UserPreference);  dislist = empty list; for\(i\gets 1\)tolen(HR)do
3 ifUser = New Userthen
4 // Extract recipe features
5 KeyWord[i] = TF-IDF(HR[i]);  RecipeProfile = Top-N(KeyWord); // Extract user features
6 Feedback = inverted-index(KeyWord); InterestWord = TF-IDF(Feedback,UserPreference); UserProfile = Top-N(InterestWord); distance = dis(RecipeProfile,UserProfile);
7
8 else
9 distance = LFM(UserPreference,HR[i]);
10
11 dislist = add(dislist, distance);
12
13 topK = GetMinK(dislist); R = GetResult(HR, topK);
14
15 FR = adjust(R, AdequcyFactor); return FR;
```  
**Algorithm 1**HFRR Algorithm
# PEiD: Precise and Real-Time LOS/NLOS Path Identification Based on Peak Energy Index Distribution
## 4 PEID: LOS/NLOS Identification Based on Peak Energy Index Distribution  
Based on the observation of peak energy index distribution, we propose two identification methods in this section. First, a threshold-based approach is given to examine the effectiveness of the STD. Second, we manually design a set of features and improve the identification accuracy with the help of machine learning. The feature importance is estimated to reveal the contributions of each element. Finally, we will introduce a sliding-window-based method to adapt our model to mobile scenarios, meeting the requirement of real-time LOS/NLOS identification.  
Figure 3: Distributionof the SIPE obtained using different devices: (**a**) Dell laptop (Alienware) in LOS case (STD = 0.584); (**b**) Dell laptop (Alienware) in NLOS case (STD = 1.836); (**c**) Thinkpad X1 Carbon laptop in LOS case (STD = 0.326); (**d**) Thinkpad X1 Carbon laptop in NLOS case (STD = 0.568). Both experiments are performed in Lab 1 mentioned above.  
Figure 2: Distribution of the SIPE obtained in different scenarios: (**a**) LOS case in Lab 1 (STD = 0.411); (**b**) NLOS case in Lab 1 (STD = 0.876), (**c**) LOS case in Lab 2 (STD = 0.482); (**d**) NLOS case in Lab 2 (STD = 0.862). In each configuration, we extract the peak energy time index of 300 packets.