esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Results  
### Gardureella Genomes Contain Prophage Genes That Encode Active Endolysins  
Although no bacteriophages of _Gardureella_ species are known, multiple _Gardureella_ genomes that have been sequenced contain DNA regions that are predicted to be of prophage origin [40; 41]. To identify endolysins that might specifically target _Gardureella_, we used Basic Local Alignment Search Tool (BLAST) to search for sequences with similarities to known endolysin genes in the prophage regions of published _Gardureella_ genomes. A total of 14 genes were identified that encoded 1,4-beta-N-acetylmuramidases, with sequence identities between 87 and 98% on the protein level, when compared to each other. Twelve genes encode a 306 residues protein, whereas two encode similar proteins that lack 55 residues at the C-terminus (Supplementary Figure S1). The sequences all encode domain structures common to all known endolysins: an N-terminal EAD of the glycoside hydrolase family 25 (GH25) and a CBD, comprising two CW_7 motifs, homologous to the CBD of Cpl-7 lysozyme, encoded by the _Streptococcus pneumoniae_ bacteriophage Cp-7 (Figure 1). The sequences of these putative endolysins are 67-88% identical to some proteins encoded by the genomes of _Atopobium_ but less than 70 % identical to any other endolysins, which may already indicate a high specificity for the genus _Gardureella_ (data not shown).  
We expressed the 14 predicted endolysin genes (EL1-EL14) as recombinant proteins in _Escherichia coli_, of which ten (EL 1-7 and EL 10-12) yielded sufficient protein to perform further experiments. Their lytic activities were tested on representatives of the four _Gardureella_ species: _G. vaginalis_ (ATCC 14018T), _G. leopoldii_ (UGent 09.48), _G. piotii_ (UGent 18.01T), and _G. swidsinskii_ (GS10234). Bacterial suspensions of 107-108 CFU/mL were incubated with each of the 10 endolysins (at 20 \(\mathrm{\SIUnitSymbolMicro g}\)/mL) or with buffer as a negative control. After a 5 h incubation, we plated the suspensions and quantified the surviving bacteria, measured as colony-forming units (CFU)/mL (Figure 2). Most of the endolysins reduced the CFU/mL of all four _Gardureella_ species by multiple \(\mathrm{log}_{10}\) units. Only EL5 and EL6 (one of the two truncated proteins mentioned above) were completely inactive in this assay. EL3 and EL10  
Figure 1: Schematic domain structure of the predicted _Gardureella_ prophage endolysins belonging to the class of 1,4-beta-N-acetylmuramidases. EAD, enzymatic active domain or H-domain; CBD, cell-wall binding domain or B-domain.  
were the most active against all four _Gardnerella_ species tested, _G. swidsinskii_ GS10234 being the most susceptible with a \(>5\log_{10}\)-fold reduction in CFU/mL (Figure 2).
[PERSON] and [PERSON] documented cheese production methods and optimal practices [4]. Today, cheeses showcase numerous unique tastes, textures, scents, rinds, and shapes, which are shaped by their biochemical composition, manufacturing techniques, the aging process, and culture [5]. It is believed that over 500 unique types of cheese are presently manufactured across the globe, of which 207 have protected denomination of origin (PDO) and 70 have protected geographical indication (PGI) recognition in the European Union (EU) [6]. Despite many efforts to categorize the diverse range of cheeses, they are generally grouped by species, coagulants (Figure 1), texture, or ripening agents [3].  
According to FAO [7], global milk production was close to 950 million tons in 2023--an increase of 1.3% compared to 2022--from which around one quarter was used for cheese production [8], thus requiring a significant amount of energy to meet such an increase.  
Although there is a vast array of cheese types, the manufacturing process follows a generally similar method (Figure 2). The initial phase in production involves transporting milk to the factory at low temperatures (4-7 \({}^{\circ}\)C). In large-scale production, the solids content must be adjusted to achieve the desired fat-in-dry matter level and fat-to-protein ratio by utilizing creaming, incorporating skimmed milk, or adding milk powder. Pasteurization or other heat treatments might also be implemented to meet hygiene standards. Nonetheless, such standardization and thermal processing methods are almost absent among most small-scale producers utilizing raw milk. The third step includes the coagulation of casein to create a gel through the processes of acidification, proteolysis, or heating along with acidification. Subsequently, the curd is extracted from the whey, shaped, and salted. The ripening process initiates a series of biochemical alterations that affect texture, aroma, and flavor, increasing the sensory complexity of each cheese. Nonetheless, depending on the cheese variety, particular environmental conditions in the ripening room must be fulfilled, specifically, ventilation, temperature, and humidity [8].  
Cheese production usually necessitates thermal energy, mostly from fossil sources and electrical power. Thermal energy, which is produced from natural gas or propane, is employed in methods such as pasteurization, coagulation, and sanitation, whereas electricity is used in mechanical processes and lighting [8].  
Previous reports on energy use in Dutch dairy farms pointed to milk pasteurization and cheese storage as accounting for the highest share of the overall energy consumption--above 40% of the total energy use. Likewise, studies regarding the cheese sector in California have highlighted thermal processes (pasteurization, heating systems, evaporators, dryers, and sterilization) as the most energy-intensive methods, making up 40% of the total energy use, whereas refrigeration processes constituted 20% of the overall energy consumption [8].  
Figure 1: Diversity of rennet-coagulated cheeses [3].
# Unsupervised Detection of Multiple Sleep Stages Using a Single FMCW Radar
## 3 Sleep Stage Detection
### Detection of REM Sleep Stage  
An automatic sleep-stage detection method, in which sleep stages are manually identified, was proposed to address the inconvenience associated with PSG. First, utilizing the characteristics of the _REM_ stage and increased respiration rate compared to the non-_REM_ sleep stage, the subject's _REM_ sleep stage, \(REM(t)\), is primarily identified as follows:  
\[REM(t)=\begin{cases}1,&\text{if }RR(t)>RR_{thres}\\ 0,&\text{otherwise}.\end{cases} \tag{7}\]  
where \(RR(t)\) is the respiratory rate measured per minute and is estimated from the radar signals acquired during sleep. The respiratory rate was calculated using a zero-crossing algorithm that detected the point at which the amplitude of the breathing signal passed zero at the radar-based characteristic distance. \(RR_{thres}\) represents the subject's ordinary respiratory rate assigned to distinguish the _REM_ sleep stage and was set as follows using the mean and variance of the breathing rate for the entire sleep duration.  
\[\begin{split} RR_{thres}=&\frac{1}{T}\cdot\sum_{t=1}^{T }RR(t)\\ &+\sqrt{\frac{1}{T-1}\cdot\sum_{t=1}^{T}|RR(t)-\frac{1}{T}\cdot \sum_{t=1}^{T}RR(t)|}^{2}\end{split} \tag{8}\]  
The results of using the _REM_ sleep-stage-detection algorithms suggested by (7) are shown in Figure 2. The intervals with a breathing rate above the threshold were marked as the _REM_ sleep section, as shown in Figure 2b, and the resulting graph is shown in Figure 2c. In Figure 2c, the interval where the PSG reading results shown in Figure 2a differ from the radar-detected _REM_ sleep-stage results is indicated in blue. This interval is where false detections occur because of the increased respiration rate caused by movement. Thus, the subject's body movement during sleep needs to be detected and utilized as an additional feature factor to improve the accuracy of _REM_ sleep-stage detection.  
Figure 1: Comparison of breathing signals. (**a**) Intermediate frequency signals of the radar. (**b**) Comparison of respiratory signals acquired with the radar and breathing sensors.
## 5 Conclusions  
This paper introduces an innovative automatic resistance identification and warning system specifically designed for pipe jacking construction. First, an analysis of the resistance-impacting factors during the pipe jacking construction process was conducted, drawing upon a review of the existing literature. Considering the varied factors influencing pipe jacking resistance, it was suitable to choose the Persson calculation model based on Persson theory from several existing resistance calculation models. On the basis of the Persson model, the calculation formula for pipe jacking friction resistance was given. Second, an automatic resistance identification and warning system was proposed that encompasses several key aspects:  
1. Sensor placement is strategically determined by analyzing the stress patterns in the soil surrounding the pipe jacking.
2. The foundational structure of the resistance identification and warning system is constructed.
3. The control process for the resistance warning is provided.
4. A construction approach during pipe jacking is presented, which integrates with the resistance identification and warning system.  
Third, the resistance identification and warning system was tested through a practical application in the Hubei Han River Black and Odorous Water Body Comprehensive Treatment Project in China. It was found that at the beginning stage of pipe jacking construction, as the jacking distance increases, the jacking resistance continuously increases, which supports the findings of a study conducted by [PERSON] et al. [23; 24; 30]. When the friction resistance value reaches the preset warning value, an alarm is triggered. After adjusting the proportion of thixotropic mud, the resistance value starts to decrease.  
In summary, the development of an automated pipe jacking friction resistance identification and warning system can provide a reference for the development of intelligent pipe jacking and contribute to improving construction safety levels. Because the warning value is established from construction experience data, further research is needed to study the application of artificial intelligence algorithms, such as deep learning, for the identification and prediction of resistance thresholds.  
Conceptualization, L.-H.J. and B.-J.W.; methodology, L.-H.J. and B.-J.W.; validation, L.-H.J.; writing--original draft preparation, B.-J.W.; writing--review and editing, L.-H.J., X.-Z.Z. and S.C. All authors have read and agreed to the published version of the manuscript.  
The authors acknowledge financial support from the National Natural Science Foundation of China (No. 52179136).  
Not applicable.  
The data presented in this study are available in Development and Field Application of an Automated Pipe Jacking Friction Resistance Identification and Warning System in Construction.  
The authors declare no conflict of interest.
## 3 Materials and Methods
### Experimental Setup
#### 3.4.3 Baseline Methods  
To comprehensively evaluate the performance and advantages of the proposed method, several classical privacy-preserving techniques were selected as baseline methods, including DP [47], FL [48], secure multi-party computation (MPC) [49], and HE [50]. DP protects sensitive information by adding noise to data or models. Its core principle ensures that the influence of individual samples is negligible through randomization. In this study, Gaussian noise addition was employed with a privacy budget parameter of \(\epsilon=1.0\). Detailed experiments were conducted to analyze the trade-offs between privacy levels and model performance. FL protects privacy by training models locally on devices and sharing only model parameters rather than raw data. A gradient aggregation-based FL framework was adopted, and further experiments were conducted to evaluate its resilience under scenarios of participant dropout and data heterogeneity. To optimize communication overhead, model updates were compressed before transmission, resulting in a 15% reduction in communication costs without compromising model accuracy. Secure multi-party computation ensures collaborative computations among multiple parties under a trustless environment using cryptographic techniques and distributed computation. This study implemented a Shamir's Secret Sharing protocol, focusing on its communication efficiency and computational scalability under varying numbers of participants. HE, which allows computations on encrypted data, provides the highest theoretical security but incurs significant computational overhead. The Paillier HE algorithm was utilized, with a detailed analysis of encryption and decryption delays during training. A batch processing optimization strategy was introduced, reducing the computational overhead by 20% compared to the standard Paillier implementation. By comparing these baseline methods, the proposed framework was demonstrated to offer superior privacy protection, model utility, and computational efficiency.
# A Method for Predicting Radiated Acoustic Field in Shallow Sea Based on Wave Superposition and Ray
## 2 Theoretical Model  
The proposed theoretical model consists of the combination of wave superposition theory and ray theory, corresponding to the acoustic inverse problem and the acoustic positive problem respectively.
# Application of Machine Learning Algorithms for Prediction of Tumor T-Cell Immunogens
## 2 Materials and Methods
### Machine Learning Methods  
Multiple machine learning (ML) methods were employed in this study, as enumerated below. The scikit-learn library [29], implemented in the Python 3.7 programming language, was utilized for model construction. In order to achieve optimal results, every model underwent hyperparameter tuning using the Grid search technique [30], which is also implemented in the scikit-learn library. It is a process that searches exhaustively through a manually specified subset of the hyperparameter space of the targeted algorithm. The hyperparameters found to be optimal for every model are specified in Supplementary File S1. Each one of the models was designed to process a series of auto- and cross-covariance (_ACC_)-transformed amino acid sequences as input. The objective of each model was to predict the antigenic or non-antigenic classification of each peptide. Accordingly, the output for peptides predicted as antigens was designated as 1, while for those predicted as non-antigens, it was designated as 0.
# Situation Assessment of Air Traffic Based on Complex Network Theory and Ensemble Learning
## 4 Experimental Setup and Details
### Ensemble Learning Model
#### Bayes model  
We adopt 10-fold cross validation. After model parameters were optimized by the grid search method, the average classification accuracy score was 0.7936. The prior probability of each sample corresponding to the positive and negative classes were output, as shown in Table 6. The _GaussianNB()_ in sklearn was called to build this model. And _predict_pro()_ was used to calculate each sample.
## 3 Error Model and Cross-Identification Algorithm
### Geometric Parameter Error Model
#### 3.1.1 Differential Transformation Matrix \(\delta_{0}^{n}T\)  
Let the parameter deviations of joint \(i\) be \([\Delta\theta_{i},\Delta\alpha_{i},\Delta\beta_{i},\Delta a_{i},\Delta d_{i}]\). Then, the errors between the actual and nominal transformation matrix of adjacent joints can be approximated as a linear combination of the link parameter deviations:  
\[d_{i-1}^{i}T=\frac{\partial_{i-1}^{i}T}{\partial\theta_{i}}\Delta\theta_{i}+ \frac{\partial_{i-1}^{i}T}{\partial\alpha_{i}}\Delta\alpha_{i}+\frac{\partial _{i-1}^{i}T}{\partial\beta_{i}}\Delta\beta_{i}+\frac{\partial_{i-1}^{i}T}{ \partial a_{i}}\Delta a_{i}+\frac{\partial_{i-1}^{i}T}{\partial d_{i}}\Delta d _{i} \tag{6}\]
## 3 Wireless Sensor Networks
### Energy Harvesting and SPWS Perpetual Operation
#### 3.4.1 Average Energy Harvested  
It was first determined the average current produced by the PV panel employed, under a range from 500 to at least 2500 Lux, typically found in room buildings. Ten equal panels were subject to different illuminance levels (637, 1493, 1998 and 2492 Lux) and the average instantaneous currents produced are shown in Figure 11; the average values are represented by the thicker line and were 185.3, 264.2, 321.6 and 382 \(\upmu\)A. Assuming that this is maintained during only 8 h per day, the average production for the illuminance values considered will decrease to 61.8, 88.1, 107.2 and 127.3 \(\upmu\)A.
# Application of Low-Altitude UAV Remote Sensing Image Object Detection Based on Improved YOLOv5
## 3 Materials and Methods
### Improved YOLOv5
#### 3.3.4 Other Tricks  
The YOLOv5s model has three detection feature maps, which are obtained from 8, 16 and 32 times of down-sampling respectively. The feature maps are 76 \(\times\) 76, 38 \(\times\) 38 and 19 \(\times\) 19, respectively. The small feature map is used to detect the large target, and the large feature map is used to detect the small target. This paper tried to replace feature maps of different depths for splicing, so that the feature map paid more attention to the size of the target in the data set. However, due to the height change of UAV, the proportion of the target in the image changed greatly, so the experimental results were not ideal. Finally, the original network splicing method is adopted.  
Anchor boxes of different sizes and proportions are set for feature maps of different sizes in YOLOv5s model. These anchor boxes are used to frame the target object. Through labeling, it can be found that the ratio of label width and height in the data set of this experiment is roughly distributed at 1:2 and 2:1. Therefore, it is necessary to modify the size of anchor boxes according to its own data characteristics before training. In this experiment, the size of anchor boxes 33 \(\times\) 23 in 78 \(\times\) 78 feature map was changed to 33 \(\times\) 16, and the size of anchor boxes 116 \(\times\) 90 and 373 \(\times\) 326 in 19 \(\times\) 19 feature map was changed to 116 \(\times\) 60 and 350 \(\times\) 180, respectively. The size of other anchor boxes basically conforms to the ratio of label width to height, so no modification will be made.
## 5 Experiments
### Memory Prediction
#### 5.2.3 Results and Analysis  
Table 3 shows the experimental results for memory prediction. We can see that our method outperforms all the baselines in predicting recall probabilities with the lowest MAE and MAPE, which illustrates that THLR has remarkable effectiveness in predicting recall probability. To show the significance of our method, we also conduct a Wilcoxon Signed-Rank Test [34] over baselines w.r.t. the two measurements. The \(p\)-value is 0.03125, which shows that we should reject the H0 assumption (the algorithms are equal) at the 95% confidence level.  
Among all baselines, Pimsleur and Leitner are traditional, rule-based models that do not adapt to individual learning patterns, resulting in the highest MAE and MAPE values. This verifies that it is difficult to learn a good memory prediction model using handcrafted rules from real-world data. HLR introduces a parameter to measure memory strength and provides recall probabilities based on the half-life and time interval, which improves the prediction accuracy but still falls short compared to more sophisticated models. DHP and GRU-HLR are more advanced models that consider more environment variables. DHP takes half-life, recall probability, recall result, and item difficulty into consideration and utilizes handcrafted transition equations. GRU-HLR utilizes recurrent neural networks to update internal parameters, which improves the flexibility compared to DHP. Thus, GRU-HLR shows a slightly better result compared to DHP. The two models both show significant improvements over the traditional models.  
THLR, the proposed model in this paper, outperforms all baselines by a considerable margin. The use of the Transformer network allows the effective capture of temporal dynamics in learning trajectories, which is crucial for accurate recall probability estimation. The results suggest that the THLR model's ability to flexibly adapt to various memory patterns and its effectiveness in time-series prediction contribute to its enhanced performance. The lower MAPE and MAPE values signify that the THLR model is better at estimating recall probabilities.
## 2 Materials and Methods
### Instrumentation  
Weight was measured using the SEGA 804, which is a precise portable electronic scale. The participants' gaits were analyzed on a ProForm 500 ZLT motorized 2.0 CV treadmill with a 0% incline, using the predetermined treadmill program at a constant speed of 4 km per hour for 30 s. The Optogait (r) photoelectric cell system was used to collect the spatio-temporal gait parameters. Optogait (r) is composed of photoelectric cells sitedalong transmitting receiving bars of 1 m in length that can be extended to 100 m with a maximum distance of 6 m between them. The transmitting-receiving bars contain infrared LED diodes, which enable communication between the two bars. When a subject passes between the transmitting bar and the receiving bar, the system automatically calculates the spatio-temporal parameters by sensing interruptions in communication. This tool has optical sensors that operate at a frequency of 1000 Hz to detect the spatio-temporal parameters that are related to walking, running, and other movements. The software used was Optogair v.1.11.1.0. The intraclass correlation coefficients of this instrument were from 0.933 to 0.999, including all of its space-time variables [20].
# Quantification and Analysis of Carrier-to-Interference Ratio in High-Throughput Satellite Systems
## 4 Influencing Factors and Selection Strategies of the CIR
### Selection Strategies  
The premise of quantifying the CIR mentioned above is that the locations of the target user and interfering users are known. However, in mobile application scenarios, the location of any user is random and time varying. Therefore, the selection of CIR values is a practical challenge. The specific selection strategies are as follows:  
1. Select typical values: This method is the most simple and feasible, but it cannot truly reflect the characteristics of the system. The link budget based on this method can only estimate the system capacity roughly rather than predict it accurately.
2. Take values according to the antenna pattern and coverage requirements: According to the actual pattern of each antenna and previous experience, for a certain beam, we can select the CIR value under a 100% coverage requirement (i.e., the CIR for the edge user, which is the worst), or under an 85% coverage requirement or under other coverage requirements. This method can take both the actual situation and engineering experience into account and is highly practical. However, it requires the real pattern of each antenna as the input, which is not convenient for theoretical research and model demonstration.
3. Calculate values statistically: The Monte Carlo simulation method is applied to randomly generate the locations of the target user and interfering users. In addition, we assume that there is only one user working in each interference area. The CIR in this specific scenario can be calculated by the quantification method proposed before. Then, this procedure is repeated over and over again. Finally, the probability density function (PDF) or cumulative distribution function (CDF) of the CIR can be obtained by statistics, and the value range of CIR can also be known. Generally, the CIR with the largest occurrence probability is selected. The detail process is shown in Figure 9.  
Figure 8: Example of the antenna radiation pattern.  
Figure 9: The detailed process of the Monte Carlo simulation method.