esa-sceva/satcom-chunk-collection huggingface dataset:
# Closing Force Evaluation of a Sample Return Capsule for a Phobos Sample Return Mission
## 5 Closing Force Measurement
### Piston-Conical Shape
Figure 15: Closing force evaluation at environmental temperature for (**a**) Type I seal and (**b**) Type II seal (peak values highlighted by black triangles). As expected, the closing force increased when the operating temperature dropped. Similar to the test performed at +20 \({}^{\circ}\)C, the graphs tendency is kept, Type I seals requiring higher forces than Type II. From Figure 17, it can be observed that there is a difference of around 500 N between the maximum closing force for Type I seals and Type II seals. For a better evaluation of the temperature effect over the closing force, it was decided to conduct a test at \(-60\)\({}^{\circ}\)C, a temperature that is lower than the closing mechanism operational temperature (\(-45\)\({}^{\circ}\)C). After the test was conducted at \(-60\)\({}^{\circ}\)C, a maximum force of 2266 N was determined for the Type I seal and 1419 N for the Type II seal, as can be seen from Figure 18.  
The analysis of the results obtained for the closing test performed at \(-60\)\({}^{\circ}\)C reveals that the tendency between the two closing force curves is kept and the difference between the peaks of the two forces is around 850 N.  
Considering all the results obtained for the piston with a conical shape, an interpolation was performed. Figure 19 presents the closing force/temperature dependency for Type I and Type II seals. One can observe that the differences between the two types of sealing elements are kept on all the temperature domains, and the results obtained for this type of piston have a large influence over the closing and sealing mechanism final design. For a better overview, the test results (load vs. displacement) for all temperature cases are plotted in Figure 20.  
Figure 17: Closing force curves obtained for \(-20\)\({}^{\circ}\)C.  
Figure 18: Closing force curves were obtained for \(-60\)\({}^{\circ}\)C.  
The obtained results are considerably higher than the ones provided by the theoretical analysis (performed based on sealing elements specifications) and the force which shall be overcome by the mechanical press cannot be ensured only by three compression springs. From this analysis, it was concluded that the maximum pushing force of the piston occurs when the piston starts to enlarge the sealing elements. For decreasing the value of this force, a piston with a new geometrical shape (toroidal shape) was designed and manufactured.
## 4 Conclusions  
In localizing minute surface defects on insulators, conventional object detection methods have encountered several limitations, with their comparatively slower processing speeds standing out as a prominent issue. This paper proposes a novel insulator surface defect detection algorithm, HST-Net, based on the improved YOLOv5, by reconstructing the backbone network structure of YOLOv5 and introducing Transformer-CSP modules as replacements for the original C3 modules. A real case is used to verify the effectiveness of the proposed HST-Net. Specifically, the ablation experiments demonstrate the proposed HST-Net achieves a near increment of 5% mAP and outperforms other object detection algorithms. These significant enhancements in detection accuracy are achieved without compromising the detection efficiency. Furthermore, we restructure the head segmentation network, enhancing the output of insulator defect segmentation. It attains an AP50 of 0.82, surpassing the OCRnet. Additionally, this method exceeds the performance of the classic segmentation network Unet. Meanwhile, we manage to maintain model lightness with a detection speed that reaches 83.33 FPS, satisfying the substation's requirements for inspection efficiency.  
For future work, we intend to incorporate more samples of various types of insulator defects to train our network. Additionally, addressing the high rate of missed detections in small insulator defects will be a significant direction for our future research.  
Methodology, J.G.; Software, T.L.; Validation, J.G.; Investigation, B.D.; Writing--original draft, T.L.; Supervision, B.D. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
Data Availability Statement: Data will be made available on request.  
The authors declare no conflict of interest.
## 2 Materials and Methods
### Experimental Design  
The formation of EBs and OGCs at various oxygen tensions was characterized (Figure 1). EBs were classified into two categories according to the stage of differentiation: simple EBs and cystic EBs [7; 8; 9]. When ES cells were cultured in suspension for 2 to 4 days, spherical ES cell aggregates were formed. These ES cell aggregates with morula-like structures were called simple EBs. After 4-5 days of continuous suspension, a cavity was formed in the center of each EB. This stage of EBs was called cystic EBs. Cystic EBs resemble follicular embryos or embryos at the egg cylinder stage, with a double-layered structure surrounding the cavity. After 8 to 10 days in suspension culture, the cystic EBs expanded into large cystic structures resembling the internal yolk sac of a post-implantation embryo. [PERSON] et al. [10] suggested that these large cystic EBs correspond to the late stage and should be differentiated from EBs up to day 5. Therefore, we decided to change the oxygen concentration from day 5, corresponding to the late stage of EBs, referring to the differentiation process of actual fertilized eggs. In order to form EBs in this study, we performed the modifications detailed by [PERSON] and [PERSON] [11]. Briefly, miPSC cell colonies were dissociated using 0.25% trypsin-EDTA (Gibco, Grand Island, NY, USA) into single cells that were resuspended in EB formation medium consisting of high glucose DMEM, 10% fetal bovine serum (FBS) (Biosera, Nuaille, France) and 10% KSR. Cells were used at a concentration of 3000 cells per 20 \(\mu\)L to form EBs using the hanging drop method for 48 h at 37 \({}^{\circ}\)C, 5% CO\({}_{2}\) and 21% O\({}_{2}\). This day is considered Day 0 of the study. Subsequently, EBs were transferred to low-attachment bacterial culture dishes in EB formation medium and were grown as suspension cultures for 72 h at 37 \({}^{\circ}\)C, 5% CO\({}_{2}\) and 21% O\({}_{2}\). Our preliminary study found that the undifferentiated characteristics of EBs remained until Day 5 (unpublished data). The EBs were then divided into three oxygen tension groups on Day 5, a hypoxia group (2% O\({}_{2}\)), a normoxia group (21% O\({}_{2}\)) and a hyperoxia group (35% O\({}_{2}\)). The oxygen levels in cell cultures were controlled using a multi gas incubator (SMA-30; Astec, Fukuoka, Japan). Five days after the change to each of the oxygen tensions, i.e., on Day 10 from the EB formation, 20 EBs were seeded in gelatin-coated 35 mm cell culture dishes to allow the formation of OGCs, and the medium was switched to MEM Alpha (Gibco, Grand Island, NY, USA) with 10% FBS. Cells were then maintained until 16 days after the change of oxygen tension, i.e., Day 26 from the EB formation, with medium changes every 2 days. EBs and OGCs were evaluated on Days 10 and 26, respectively, using qRT-PCR and immunocytofluorescence (ICF) staining.
## 4 SNR Characterization
### RSOA-FCL Performance
This is supported by the large noise level in the received electrical DMT spectrum of the 1 km RSOA-FCL that can be seen in the inset of Figure 7c (red curve). However, the negative impact of the dispersive cavity on the SNR can be avoided by using O-band RSOAs. In the O-band where chromatic dispersion is minimum, RSOA-FCL cavity could be extended beyond 1 km [22; 53]. Finally, we investigated the SNR after transmission over 25 km NZDSF (chromatic dispersion: \(\sim\)5 ps/nm\(\cdot\)km). This fiber is used to emulate a low dispersion PON fiber--similar to what one might expect if the system were operated in the O-Band. The probed SNR for the three cavity lengths for both a BtB and a 25 km transmission are plotted in Figure 8. When comparing the BtB and 25 km SNRs, the maximum SNR values for the respective cavity lengths did not show major change. Rather, the usable SNR bandwidth was reduced to \(\sim\)3.3, 3.2 and 2.8 GHz for the 15 m, 115 m and 1 km cavities, respectively. The results are summarized in Table 2. This shows a 40-50% bandwidth reduction compared to the BtB case. The finite signal bandwidth of the directly modulated RSOA results in chirp after propagation that induces power fading and bandwidth reduction. In addition, such highly multi-mode source suffers from mode partition noise induced by the NZDSF dispersion which increases the RIN.
# Study on Deformation Characteristics of Low-Highway Subgrade under Traffic Load
## 3 Test Results
### Attenuation Rule of Dynamic Modulus  
Figure 17 shows the curve of dynamic modulus versus cumulative strain under different dynamic loads, and the normalized dynamic modulus ratio is \(E_{d}/E_{\max}\). It can be seen from the figure that the dynamic elastic modulus decreases with the increase in axial strain, and the curve shows a nonlinear change trend. The dynamic modulus decreases to about 80% of the total reduction at the initial loading stage when the soil sample produces a 20% strain variation. Then, the attenuation rate of the curve slows down until it reaches a stable state. The attenuation degree of the dynamic elastic modulus is inversely proportional to the buried depth of soil. The shallower the subgrade depth, the greater the attenuation amplitude of the dynamic modulus. Taking Figure 17a as an example: when the axle load = 50 kN, the dynamic-modulus ratios of 0.5 m, 1.5 m, and 2.5 m depth are 0.9102, 0.9304, and 0.9391, respectively, that is, the closer to the subgrade surface, the more pronounced the modulus reduction. The farther away from the pavement, the earlier the modulus reaches the stable stage, and the smaller the required strain.  
Figure 16: Pore-pressure-ratio variation under different depths. (**a**) Depth = 0.5 m; (**b**) Depth = 1.5 m; (**c**) Depth = 2.5 m.  
Figure 17: Cont.  
There is a hyperbolic relationship between the dynamic-modulus ratio and the cumulative strain. The curve is fitted by Formula (2). The critical parameters of the hyperbolic model under test conditions can be obtained, as shown in Table 4.  
\[\sigma_{d}=\frac{\varepsilon_{d}}{\mathrm{b}+a\varepsilon_{d}} \tag{2}\]  
where, \(\sigma_{d}\) is the dynamic stress; \(\varepsilon_{d}\) is the dynamic strain.
## 5 Field Monitoring
### Layered Backfill Monitoring  
In order to ensure the safety of the tunnel after backfilling, the settlement of layered backfill soil is monitored at d3k279 + 915, as shown in Figure 13. This paper focuses on monitoring the settlement of backfill near the underground river, so the monitoring range is 20-40 m below the tunnel floor, and a monitoring point is set every 2 m.  
At the initial stage after backfilling, the backfill body of each layer is gradually consolidated under the action of self-weight and upper construction load, displacement fluctuating frequently within the range of 5\(\sim\)-10 mm (positive value indicates soil uplift).  
After 15 January 2019, the backfilling and compaction stage were completed, and the project entered the tunnel construction process. The first step was the tunnel step excavation. There was obvious settlement to backfill of each layer of d3k279 + 915, of which the settlement at 20 m is the largest, reaching 53 mm, and the settlement of each layer surrounding rock was concentrated in 40-50 mm. During this period, the settlement of each layer basically decreased with the increase of depth, which corresponded to the actual situation.  
After the completion of tunnel excavation, grouting reinforcement was carried out for the backfill areas at the bottom and both sides, so the settlement of surrounding rock of each layer tended to decrease. Among them, the displacement of deep surrounding rock decreased significantly, and the maximum reduction degree was 10 mm. Therefore, it can be considered that grouting to the bottom of the tunnel can effectively improve the surrounding rock's mechanical condition.
## 6 Discussion
### In the Aspect of Model Testing Performance  
As Figures 8 and 9 show, all CNN prototypes had poor testing accuracy that was lower than their TL forms, and the highest accuracy among prototypes was just 81% for the VGG16. The VGG16-FT achieved the best accuracy of 98%, and the other three models of the VGG16-BF, InceptionV3-BF, and Xception-BF all had good accuracy of over 95%. For testing speed comparison, CNN-FT models were slightly faster than the prototypes while CNN-BF models were evidently slower than both the prototypes and CNN-FT models. The VGG16-FT also had the fastest testing speed of 41.1 images/s, thus just besides the prototype of VGG16, the VGG16-FT had a significant speed advantage (nearly double the speed of other models). Summing up, this comparison study showed that TL forms of CNN have advantages in testing accuracy, and CNN-FT models have advantages in testing speed.  
Summing up, it can be concluded that the VGG16-FT was optimal on the whole, as it had the highest accuracy, fastest operation speed, and best training convergence. Hence, based on this comparison study, the VGG16-FT is recommended as the most suitable model for the proposedvision-based FEQS method. Meanwhile, it can be seen from the discussion of results that for the full/empty-load truck binary classification problems, TL forms of CNN have advantages to CNN prototypes because CNN-BF models are generally better in model training and both CNN-BF and CNN-FT models are better in model testing than prototypes. As the testing accuracy results of CNN prototypes all are lower than 85%, adopting CNN prototypes in actual application needs consideration, because poor accuracy fails to reach the working goal. For TL forms of CNN, CNN-BF models have advantages in both training and testing accuracy, CNN-FT models have advantages in testing speed and are also good in testing accuracy.  
Hence, model choice suggestion in applications can be provided: The adopted model should be chosen according to the working demands and conditions, i.e., when the working accuracy is of first priority, CNN-BF models can be recommended as they are better in accuracy; when real-time working is required, CNN-FT models can be recommended as their fast operation speed can reduce the working time delay.
# An Adversarial DBN-LSTM Method for Detecting and Defending against DDoS Attacks in SDN Environments
## 3 Proposed System
### Abnormal Defending  
Once the adversarial DBN-LSTM anomaly detection module detects a DDoS attack, this module will execute an exception prevention mechanism. Based on event-condition-action (ECA), we can set the corresponding trigger rules for the SDN controller at the application layer. Once the previous module detects a DDoS attack, it will send a message to the SDN controller to drop corresponding packets. We will set a list to record these IPs' ports to avoid some normal packets being discarded, similar to DDoS attacks. The mitigation process is summarized as follows in Algorithm 2.  
```
0: Suspect flows \(\beta_{t}\)
1: Identify the suspect flows based on IP addresses and ports that make the analysis interval anomalous
2: Identify the destination IP address that receives the most flows
3: Identity in those flows the attackers' IP address, which has the same destination port
4: If IPs e ports are on the Safe List then
5: Forward packets
6: Else
7: Drop packets
8: End if
```  
**Algorithm 2** Abnormal Defending Process
## 4 Discussion of CPMA Application
### Multi-Machine Monitoring Result Visualization in CPMA  
Finally, the proposed CPMA was applied to monitor two CNC lathe machines at different locations. Figure 15 shows the arrangement scheme of the application. To support this application, the CPMA website was updated to include a second page to monitor different machines.  
For this implementation, an external cylindrical dry turn was performed on two CNC lathes (Vturn-A20). A 60 \(\times\) 100 mm AISI1045 round bar was used for the machining tests. The machining process comprised a simple roughing operation with a cutting depth of 0.5 A mm, a feed rate of 0.1 mm/rev, and a cutting velocity of 300 m/min along a horizontal cutting path with a length of 25 mm. This application proves that the proposed CPMA can be used to monitor different machines at different locations. Although we only present the monitoring system of two adjacent machines with the proposed system, it can also be applied to monitor two different machines in two different locations, even in different buildings or regions. This can be achieved because a specific network is not  
Figure 14: Message-sender feature.  
Figure 13: Disturbance during continuous synchronization.  
required. Instead, the system only requires Internet data on desktop users and CPMA devices. Figure 16 shows the latest images captured during the machining process in each machine.  
A MEMS accelerometer and a piezoelectric sensor were used in the first machine. Only a piezoelectric sensor was installed on the second machine. Although the system shows the latest images captured during machining, which is an idle state of machining, the overall captured data from this system can be accessed again from the drive system in every folder of the local and cloud systems, as shown in Figure 17.
# Three-Stage Deep Learning Framework for Video Surveillance
## 3 Proposed Method
### Loss Function  
To train the Transformer-based framework proposed in this study, binary focal loss [34] was adopted and used as the loss function. Focal loss was first developed for object detection. Object detection is mainly divided into two types: two-stage and one-stage detectors. The difference is that a two-stage detector performs localization and classification, whereas a one-stage detector processes localization and classification simultaneously. Two-stage detectors have better accuracy performance, but they have the disadvantage of a lower speed. However, one-stage detectors have the opposite disadvantage of being faster but less accurate, and focal loss is designed to improve the accuracy. The main problem with one-stage detectors is class imbalance during training. For example, the difference between the proportions of real objects and background in an image is very large, with real objects accounting for a very small percentage of the background. Because of this imbalance, the training process frequently incorrectly predicts the presence of an object against a background. Outputting an object box in the background is learned as an error, but its frequency is so high that it interferes with learning and reduces the efficiency.  
Traditionally, cross-entropy, which focuses on penalizing incorrect predictions, has been adopted and used. However, focal loss reduces the weight of the loss by multiplying the basic cross-entropy loss by a specific weighting term. This specific weighting term is based on the model-generated prediction probabilities and dynamically adjusts the weight of the prediction errors in each class. This dynamically adjusted weighting term allows the model to focus on a smaller number of real-world entities, thus mitigating the class imbalance. The mathematical expression for this focal loss is given by Equation (2).  
\[FL(p_{t})=-(1-p_{t})^{\gamma}\log(p_{t}), \tag{2}\]  
where \(p_{t}\) is the probability that the model makes a correct prediction. Further, the \((1-p_{t})^{\gamma}\) term is a specific weighting term that dynamically adjusts the weights for prediction errors in each class based on the prediction probabilities generated by the model. In this term, the hyperparameter \(\gamma\) controls the model's focus on difficult examples. As the value of \(\gamma\) increases, the model penalizes challenging examples more, being more focused on learning these challenging examples. In Equation (2), a \(\gamma\) value of 0 implies a standard cross-entropy loss.  
In this study, we utilized the characteristics of focal loss to overcome the class imbalance problem that occurs in anomaly detection problems, where there are significantly fewer anomalies than in normal situations, and we obtained a high-performance anomaly detection model.
# A Query Understanding Framework for Earth Data Discovery
## 4 Methodology
### Spatial and Temporal Parsing  
Spatial bounding box and temporal range are two important factors in Earth data discovery. Two off-the-shelf tools are used in this framework. For geospatial parsing, an open source geocoding package [32] (Google Geocoding API client) converts the address in the unstructured query to latitudes and longitudes of spatial bounding box of the address. The Google Geocoding API [33] provides geocoding and reverse geocoding of addresses. Geocoding is the process of converting addresses into geographic coordinates to place the markers on a map [33].  
The temporal attributes of geographical metadata (e.g., release date, coverage time) are structurally formatted. However, the date in the user query is often written in unstructured natural language, which is unmatched to the format of date in the relevant metadata. The solution is Dateparser [34], an open source python library for temporal parsing. Dateparser translates specific dates (e.g., 20 August 2018) into a DateTime object as the same date format metadata use to retrieve data falling into the time range.  
Given a query, Google Geocoding API and Dateparser sequentially detect the spatial and temporal components. Although Google Geocoding API and Dateparser were selected to detect and transform spatial and temporal attributes in the framework, the two software packages are replaceable with similar open-source software, commercial software, or customized spatial and temporal parsers to accurately and efficiently parse spatial and temporal terms from a query.
# Estimating Time-Varying Applied Current in the Hodgkin-Huxley Model
## 3 Parameter Estimation and the Ensemble Kalman Filter
The observation ensemble  
\[y_{j+1}^{p}=y_{j+1}+w_{j+1}^{p},\qquad p=1,2,\ldots,N \tag{29}\]  
where \(w_{j+1}^{p}\sim\mathcal{N}(0,\mathbb{D})\) represents the observation error, is compared to the observation model predictions  
\[\hat{y}_{j+1}^{p}=G(x_{j+1|j}^{p},\theta_{j}^{p}),\qquad p=1,2,\ldots,N \tag{30}\]  
computed using the observation model \(G\) as in (23). In this work, \(G\) is a linear observation function measuring only the voltage in the Hodgkin-Huxley system. The combined posterior ensemble is then given by  
\[z_{j+1|j+1}^{p}=z_{j+1|j}^{p}+K_{j+1}(y_{j+1}^{p}-\hat{y}_{j+1}^{p}) \tag{31}\]  
for each \(p=1,2,\ldots,N\). The Kalman gain matrix \(K_{j+1}\) incorporates the cross-covariance of state and model predictions, the forecast error covariance, and the observation noise covariance. For additional implementation details, see [19; 27]. Parameter trackingIn the above formulation of the augmented EnKF, \(\theta\) is assumed to be constant and is evolved artificially with time in order to obtain an estimate. When \(\theta\) is time-varying, as in the case we are considering, the augmented EnKF as presented requires additional modification. If \(\theta=\theta(t)\) changes more slowly than the dynamics of the system, it is possible to track the changes in \(\theta(t)\) by incorporating a random walk in the prediction step. To implement this, a new random variable \(\xi\) is introduced and added to current estimate of \(\theta\) at each prediction step, allowing the previously fixed parameters to take a random walk of the form  
\[\theta_{j+1|j}^{p}=\theta_{j|j}^{p}+\xi_{j}^{p},\qquad\xi_{j}^{p}\sim\mathcal{ N}(0,\sigma_{\xi}^{2}) \tag{32}\]  
for each \(p=1,2,\ldots,N\). Parameter tracking of this type has been used in various data assimilation problems; see, e.g., [14; 15; 16; 17; 28]. Here we note that the choice of the standard deviation \(\sigma_{\xi}\) in the drift term of the random walk is important in the accuracy and uncertainty of the resulting parameter estimate. We will explore this further in the numerical experiments.