esa-sceva/satcom-chunk-collection huggingface dataset:
# An Adapter and Segmentation Network-Based Approach for Automated Atmospheric Front Detection
## 2 Data and Method
### The Training of the Network  
#### 2.3.1 The Loss of the Network  
The loss of the model consists of four components: the cross-entropy loss (CELoss) [25] and DLoss [26] for the classification branch, the regression loss for bounding boxes, and the loss for the mask branch.  
During training, the classification network module employs both the DLoss function and the CELoss function. The DLoss function measures the similarity between the network's recognition results and the sample labels, with training focusing more on the mining of frontal regions. However, it may suffer from loss saturation issues. Therefore, using DLoss alone does not yield optimal results. To address this, CELoss--which calculates the average loss per grid point--is incorporated for combined use. The functions are expressed as follows:  
\[DLoss=1-\frac{2*\left|y\cap\hat{y}\right|+smooth}{\left|y\right|^{2}+\left| \hat{y}\right|^{2}+smooth}, \tag{9}\]  
where \(y\) represents the network's frontal identification result; \(\hat{y}\) denotes the sample label; \(\left|y\cap\hat{y}\right|\) indicates the number of intersecting grid points between \(y\) and \(\hat{y}\); \(\left|y\right|\) and \(\left|\hat{y}\right|\) represent the number of grid points in \(y\) and \(\hat{y}\), respectively; and smooth is a smoothing parameter (set to 0.1 in this paper). The purpose of squaring the denominator term is to accelerate the convergence speed.  
For the classification task and mask branch, the loss calculation employs the CELoss function, which is expressed by the following formula:  
\[CELoss=-\frac{1}{N}\sum_{n=1}^{N}\sum_{C=1}^{C}y_{c}^{(n)}\log(\hat{y}_{c}^{(n )}), \tag{10}\]  
Here, \(N\) represents the number of samples, \(C\) represents the number of classes, \(y\) denotes the predicted results, and \(\hat{y}\) denotes the ground truth labels.  
The loss function formula for the bounding box regression task is expressed as follows:  
\[Loss=\sum_{i}^{N}\left(t_{*}^{i}-\hat{w}_{*}^{T}\varnothing_{5}\left(p^{i} \right)\right)^{2}+\lambda\left\|\hat{w}_{*}\right\|^{2}, \tag{11}\]  
Here, \(\varnothing_{5}\)(P) represents the eigenvector, \(t_{*}\) is the translation amount, \(w_{*}\) denotes the parameters to be learned (* indicates \(x\), \(y\), \(w\), \(l\)), and \(\lambda\left\|\hat{w}_{*}\right\|^{2}\) is the L\({}_{2}\) regularization term.
###### Abstract  
Offshore installations are subject to perpetual fatigue loading and are usually very hard to inspect. Close visual inspection from the turret is usually too hazardous for divers and is not possible with remotely operated vehicles (ROVs) because of the limited access. Conventional nondestructive techniques (NDTs) have been used in the past to carry out inspections of mooring chains, floating production storage and offloading systems (FPSOs), and other platforms. Although these have been successful at detecting and assessing fatigue cracks, the hazardous nature of the operations calls for remote techniques that could be applied continuously to identify damage initiation and progress. The aim of the present work is to study the capabilities of acoustic emission (AE) as a monitoring tool to detect fatigue crack initiation and propagation in mooring chains. A 72-day large-scale experiment was designed for this purpose. A detailed analysis of the different AE signal time domain features was not conclusive, possibly due to the high level of noise. However, the frequency content of the AE signals offers a promising indication of fatigue crack growth.  
structural health monitoring; acoustic emission; mooring chain; fatigue crack growth; structural integrity +
Footnote †: journal: Applied Physics Letters
## 2 Time Series Forecasting for Energy Reduction
### Forecasting Time Series  
A forecast is considered a prediction derived from time series data. It is an estimation of the future based on historical data and considering the time dimension. A forecast can be classified as one-step-ahead or multiple-step-ahead. In the first case, only the next value is forecast, and in the second case, multiple values are forecast in the same iteration. The amount of values to be forecast is called the _forecasting horizon_.  
Most statistical forecasting methods are designed to function on stationary time series. A time series is said to be stationary when statistical properties, such as mean and variance, are not a function of time. This considerably reduces the complexity of the models. Time series with trend and seasonal patterns are usually non-stationary by nature [11]. In such cases, in order to apply statistical forecasting methods, the first step is to transform the non-stationary time series into stationary time series by removing its trend and seasonal components.
## 1 Introduction
Therefore, an utmost effort has been made to improve the data quality and subdue the noise to make it suitable for seismic interpretation and fault analysis. A structurally oriented adaptive filtering technique was used to address the aforementioned issues, which not only suppressed coherent and random noise but also conserved fine details like edges to extract and map structural and stratigraphic features. To further improve the reflector continuity, a spectral enhancement process was applied on a noise-suppressed seismic cube. This process compensated for low- and high-end missing frequencies and improved the data quality. Next, the optimized volume blended with an ant-tracking attribute was used as input for fault detection using [PERSON]'s method. Finally, the resulting tracking volume is used to auto-extract faults with a full user's control on filtering fault surfaces based on multiple criteria such as dip, azimuth and size [13]. Modern technology and advancements in computing power are aiding geoscientists to interpret finer details such as smaller discontinuities that are difficult to resolve using a conventional interpretation methodology. This not also gives flexibility for efficient data reconnaissance but also saves a considerable amount of prospect generation time. The study area lies in a pull-apart extensional rift basin in offshore Malaysia, called the Malay Basin (Figure 2) [14]. Geologically and seismically, it represents a layer cake model with compressive anticlinal inversion structures. This is one of the deepest (12 KMs at center) basins in SE Asia and is highly petroliferous and has already witnessed an extensive hydrocarbon exploration and production [15; 16; 17; 18; 19; 20]. Keeping this context in view, this study has been carried out to device efficient and optimized techniques to understand the structural framework better. The study area is structurally an east-west trending anticline with mostly normal faults at reservoir level [21; 22]. A sizeable 3D-seismic cube of around 300 sq. Kms was used for seismic interpretation and fault analysis. Figure 2: The Malay Basin, showing the study area, play types and major faults zones. (Modified after [23]; faults added based on [24]).
## 3 Results and Discussion
### Supplementary Measurements of Magnetic Hysteresis Curves of Co-Based Amorphous Ribbons  
For practical reasons, an analysis of the magnetic properties of an initial semi-finished product precedes the final production of the core. Consequently, supplementary hysteresis measurements of three types of amorphous ribbons are presented. These are the above-discussed VITROVAC 6025, VITROVAC 6030, and VITROVAC 6150 samples, belonging to a set of Co-based metallic alloys, well known for their magnetically soft behavior and zero magnetostriction.  
Measurements were gradually performed on individual demagnetized samples with the same dimensions of 40 \(\times\) 6.5 \(\times\) 0.02 mm and a total volume of 5.2 mm\({}^{3}\). A maximum excitation current was set to 13,107.2 bits, corresponding to 0.25 A. A constant measurement step was configured to 100 bits, giving a 131 measurement point from 0 to maximum excitation currents. After each measurement step, 200 samples of the stray magnetic field were measured at a distance of 108.8 mm from the core midpoint. The measured hysteresis curves of all three samples are depicted in Figure 8 as the dependence of magnetic polarization on the applied excitation magnetic field.  
The measured magnetic hysteresis curves of all samples show properties of a typical magnetically soft material. As can be seen, the VITROVAC 6025, VITROVAC 6030, and VITROVAC 6150 samples reach a saturation value of 0.6 T, 0.8 T, and 1.0 T, respectively, which corresponds with the magnetic properties stated by the manufacturer [49]. The analysis of the linear area of the individual characteristics confirmed that the highest slope, 0.00776, corresponds to the VITROVAC 6025 magnetic characteristic. On the contrary, the lowest slope has the VITROVAC 6030, with a value of 0.00376. Regarding the VITROVAC 6150 characteristics, its slope of the linear area is equal to 0.00397. Therefore, there is an apparent reason to prefer the VITROVAC 6025 amorphous ribbon as the initial semi-finished product for the actuator core production since the higher slope of the linear area of the core characteristics leads to lower energy consumption, which corresponds to one of the most critical parameters regarding the satellite applications.  
Figure 7: Magnetic characteristic of magnetometric probe core made of VITROVAC 6025 amorphous ribbons.
# Dynamic Packet Duplication for Industrial URLLC
## 3 Proposal
### System Description  
The objective of the system described in this paper is to provide a prediction of the E2E latency to assess the need for using PD solution. The proposed system provides an estimator for downlink transmissions that is trained offline in a server running in the network edge to reduce the computing workload and memory requirements at the master node.  
The end devices may be installed in production line elements, AGVs, drones etc. These terminals may need to receive messages with a certain guarantee of latency and reliability. The reliability and latency can be improved by using multi-connectivity, that is, duplicating the packet via two paths, with the cost of dramatically increasing the amount of radio resources spent per transmission.  
An alternative way, as presented in this paper, is to first predict whether the network will be able to provide that guarantee without using PD. The MN, right before transmitting, can estimate if a regular transmission, that is, without extra resources, will be sufficient or if PD technique needs to be established. When URLLC devices are not involved in any processor-intensive task, they will measure the latency and report the measurements along with the KPIs to the system where a ML method will update the estimation model.  
Figure 2 shows the overall architecture of the system. There are three main domains: the device, which contains the sample collection modules; the server, which runs the ML algorithm and stores a dataset with solved trained cases; and the MN, which contains the estimator. The device needs to receive URLLC messages, and therefore KPIs and S-KPI are forwarded to the server, which runs the ML algorithm and is located at the network edge with computational resources and access to a non-restrictive power source.  
The server will collect the data gathered by the devices and generate the ML parameters (prediction model) for the estimator. Finally, the MN uses the estimator to predict the E2E latency of URLLC devices. Based on the prediction, MN will activate/deactivate PD for the current packet transmission.  
The functions of the different modules of the system are explained below:  
* KPI monitor: collects the KPIs from the radio interface at regular intervals.
* S-KPI monitor: reads the information from the URLLC device and measures the latency.
* Training data collector: joins the data generated by the KPI monitor and the S-KPI monitor. The data joined is used as input to train the ML model.
* Estimator: performs the task of estimating the S-KPI. The primary inputs are the current KPIs (such as SINR, MCS, HARQ feedback etc.) as measured by the MN. The output is the estimation of the E2E latency. This module also has a secondary input that consist in the estimation model extracted from the ML.
# A Network Analysis on Cloud Gaming: Stadia, GeForce Now and PSNow
## 5 Conclusions and Future Directions  
In this paper, we showed important aspects of the recently launched services for cloud gaming, namely Stadia, GeForce Now, and PS Now. Millions of users could potentially use them shortly, accounting for a large part of Internet traffic. Indeed, Stadia and GeForce Now stream data up to 44 Mbit/s, which is, for instance, much higher than a 4K Net-fix movie. However, fast 4G mobile networks can often sustain this load, while traditional 3G connections struggle. Cloud gaming applications rely on the standard RTP protocol for multimedia streaming, except for PS Now, which does not use any documented protocol.  
The services mentioned above entered the market between 2019 and 2020, and, as such, a lot of work is still to be done for characterizing them and understanding their impact on the network. Future directions include studying their infrastructures from different points of view worldwide to find similarities and differences in the deployments. Moreover, we  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline  & **Servers** & **Subnets** & **ASNs** & **Owner** \\ \hline Stadia & 74 & 22 & 15169 & Google \\ \hline GeForce Now & 37 & 23 &
\begin{tabular}{c} 11414, \\ 20347, \\ 50889 \\ \end{tabular} & NVIDIA \\ \hline PS Now & 36 & 2 & 33353 & Sony \\ \hline \hline \end{tabular}
\end{table}
Table 3: Gaming servers characterization.  
believe that it is crucial to conduct campaigns aiming to measure the subjective quality of experience (QoE) enjoyed by the users with human-in-the-loop controlled experiments, and with AI-bot players [30]. Finally, Microsoft and Amazon will release their cloud gaming platforms, namely \(\times\)Cloud, and Luna, which are in early deployment at the time of writing this article. Any future work on this topic must include them.  
Conceptualization, M.T., L.V. and D.G.; Investigation, A.D.D., G.P. and M.T.; Methodology, M.T. and A.D.D.; Software, A.D.D.; Supervision, M.T., L.V. and D.G.; Writing--Original Draft, A.D.D., G.P. and D.G.; Writing--Review and Editing, M.T., L.V. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The supplementary material containing sample traces regarding the network traffic generated while playing with GeForce Now, PS Now, and Stadia are available at [5].  
The research leading to these results is supported by the SmartData@PoliTO center for data analysis and Big Data technologies.  
The authors declare no conflict of interest.
# Probabilistic Analysis of Infinite Slope Stability Considering Variation in Soil Depth
###### Abstract  
In probabilistic slope stability analysis, soil depth has been treated as a deterministic variable, although it is a highly variable parameter. This study aims to identify soil depth variability using seismic refraction survey data and to analyze its impact on probabilistic analysis of slope stability. Seismic refraction survey data were collected from 70 slopes in South Korea and employed to identify the variability of soil depth within natural slopes. As a result, the average soil depth across 70 slopes was 2.5 m, with an average coefficient of variation (COV) of 29%, indicating high variability. To investigate the influence of soil depth variability on the probability of slope failure, probabilistic slope stability analysis was conducted by considering the shear strength parameters of soil and soil depth as random variables. Accordingly, the influences of the variability of soil depth on the probabilistic analysis of slope stability were evaluated by comparing the probability of slope failure and distribution of the failure occurrence frequency by depth. Additionally, global sensitivity analysis was conducted to understand the relative contribution of input parameters on the probability of slope failure. Consequently, the probability of slope failure can vary significantly depending on soil depth variability, emphasizing the importance of considering this factor in probabilistic slope stability analysis.  
Keywords:soil depth; seismic refraction survey; slope stability; probabilistic analysis; global sensitivity analysis +
Footnote †: journal: Applied Sciences
## 1 Introduction  
Excessive gingival display, which can be due to altered passive eruption (APE) or gingival enlargement, results in short clinical crowns; this is a cause of common esthetic concern for many patients. Gingivectomy or esthetic crown lengthening with bone resection is often required to increase the clinical crown length and achieve acceptable esthetic outcomes [1]. The decision whether to perform bone resection is largely dependent on the location of the alveolar bone crest in relation to the cementoenamel junction. If the bone crest is at, or coronal to, the cementoenamel junction, then osseous resective surgery is indicated [2]. In order to locate the bone crest, bone sounding and periapical radiograph assessments are typically performed [3, 4] However, these methods may be challenging and could provide inaccurate assessments [5]. Cone beam computed tomography (CBCT) has been suggested as a precise and reliable alternative approach for diagnosing APE [6].  
To achieve adequate esthetic results, surgical guides are often utilized, and based on diagnostic waxing, made with acrylic resin or vaccuform transparent shells [7, 8]. However, this technique has been reported to be sometimes inaccurate [9]. A recent report described a digital workflow in which digital waxing was used to design the surgical guide for crown lengthening [10]. The use of diagnostic waxing is not necessary when no restorative treatment is indicated.  
Currently, there are no known studies that have used CBCT to locate the facial bone crest and to design a digitally fabricated guide for crown lengthening surgery. Therefore, this clinical report presents a patient with altered passive eruption, for whom CBCT was combined with digital scanning to make a single surgical guide for crown lengthening.
## 3 Results and Discussion
### Effect of Grape Pomace inclusion on Dough Rheological Properties
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Sample**} & **Water Absorption** & **Stability** & **Development Time** & **Degree of Softening** & \multirow{2}{*}{**Quality Number**} \\  & **(\%)** & **(min)** & **(min)** & **(UB)** & \\ \hline
**GP0** & 55.50 \(\pm\) 0.00 a & 5.80 \(\pm\) 0.72 a & 1.40 \(\pm\) 0.10 a & 76.33 \(\pm\) 4.73 a,b & 41.67 \(\pm\) 13.05 a \\
**GP5** & 56.80 \(\pm\) 0.17 b & 7.50 \(\pm\) 0.00 b & 1.37 \(\pm\) 0.12 a & 81.33 \(\pm\) 6.03 a & 80.33 \(\pm\) 3.06 b \\
**GP10** & 60.03 \(\pm\) 0.46 c & 8.27 \(\pm\) 0.55 b & 1.40 \(\pm\) 0.10 a & 67.00 \(\pm\) 3.00 b & 87.33 \(\pm\) 6.51 b \\ \hline \hline \end{tabular} Data with different letters in each column are significantly different for \(p<0.05\).  
\end{table} 
Table 1: Farinograph characteristics of dough supplemented with grape pomace powder. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Sample**} & **L** & **P** & **G** & **W** \\  & **(mm)** & **(mm)** & **P/L** & **(cm3)** & **(10\({}^{-4}\) J)** \\ \hline
**GP0** & 106.33 \(\pm\) 11.72 a & 76.67 \(\pm\) 4.51 a & 0.73 \(\pm\) 0.12 a & 22.97 \(\pm\) 1.29 a & 268.67 \(\pm\) 8.62 a \\
**GP5** & 30.00 \(\pm\) 1.00 b & 165.33 \(\pm\) 4.62 b & 5.52 \(\pm\) 0.32 b & 12.20 \(\pm\) 0.20 b & 222.67 \(\pm\) 3.51 b \\
**GP10** & 15.33 \(\pm\) 1.15 c & 179.33 \(\pm\) 9.02 b & 11.75 \(\pm\) 1.12 c & 8.70 \(\pm\) 0.35 c & 118.33 \(\pm\) 11.72 c \\ \hline \hline \end{tabular} Data with different letters in each column are significantly different for \(p<0.05\).  
\end{table} 
Table 2: Alveograph characteristics of dough supplemented with grape pomace powder. and GP10 doughs compared with GP0 dough, as observed in doughs enriched with white grape peel flour of different particle sizes [45]. This result could be associated with polymer complexes from mixing derived from interactions between fiber and amylose and low-molecular-weight amylopectin chains [46]. Moreover, starch may have been exposed to a faster gelatinization process, leading to higher water absorption from dietary fiber of GPP [35]. On the contrary, the addition of GPP had no effect on the starting temperature of starch gelatinization and the gelatinization temperature at maximum viscosity.
## 4 Experimental Results
### Results and Analysis
#### 4.2.4 Ablation Study of Target Re-Identification  
To reflect the optimization results of several modules in TRTrack, including the bipartite matching model in the preliminary object tracking module and improved YOLOv5-DeepSORT in the boundary object detection module, extensive ablation experiments were carried out.  
* Effect of the bipartite matching model of the preliminary object tracking module  
We used the original NeighborTrack and the bipartite matching model in TRTrack to conduct comparative experiments in our own classroom video dataset CLV to demonstrate the effect of optimizing the NeighborTrack. A part of the experimental results is shown in Table 5.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \multirow{3}{*}{**Video ID**} & \multicolumn{3}{c}{**TRTrack with**} & \multicolumn{3}{c}{**TRTrack with**} \\  & \multicolumn{3}{c}{**Original NeighborTrack**} & \multicolumn{3}{c}{**Bipartite Matching Model (Ours)**} \\ \cline{2-7}  & **ET (Times)** & **ED (s)** & **TD (s)** & **ET (Times)** & **ED (s)** & **TD (s)** \\ \hline
001 & 4 & 8.74 & 6.79 & **2 \({}^{*}\)** & **2.72** & **2.64** \\
002 & 5 & 9.26 & 7.42 & **2** & **2.71** & **2.71** \\
003 & 4 & 6.38 & **5.16** & **4** & **5.27** & 5.27 \\
004 & 5 & 7.29 & 6.94 & **4** & **2.94** & **2.94** \\
005 & 7 & 12.16 & 10.07 & **3** & **3.01** & **3.01** \\
006 & 6 & 6.11 & 4.83 & **2** & **2.01** & **1.89** \\
007 & **0** & **0.00** & **0.00** & **0** & **0.00** & **0.00** \\
008 & 1 & 1.65 & 0.54 & **0** & **0.00** & **0.00** \\ \hline \end{tabular} \({}^{*}\)The bold numbers in the table represents the optimal value in the evaluation metrics.  
\end{table}
Table 5: Results of the bipartite matching model of the preliminary object tracking module.  
Figure 8: Comparison of target re-identification results. It can be seen that when targets return to the shot, except for TRTrack, other models have tracking errors, and they are unable to quickly re-identify the target.  
Table 5 shows that the bipartite matching model can enhance the ability of target re-identification of TRTrack. In most videos of the CLV dataset, the metric ET, ED, and TD of TRTrack with the bipartite matching model are superior to TRTrack with the original NeighborTrack.  
* Effect of improved YOLOv5-DeepSORT of boundary object detection module  
Apart from comparing the effects of different loss functions on object tracking, the original YOLOv5-DeepSORT and our improved YOLOv5-DeepSORT are used to conduct a comparative experiment, which is also based on the classroom video dataset CLV to demonstrate the optimized effect. A part of the experimental results is shown in Table 6.  
The effect of improving the loss function is obvious. Table 6 shows that the target re-identification ability of TRTrack based on YOLOv5-DeepSORT with the alpha-IoU loss function is significantly better than TRTrack with the original YOLOv5-DeepSORT. The metric ET, ED, and TD have apparent optimization, indicating that different loss functions have different impacts on the effectiveness of models.