esa-sceva/satcom-chunk-collection huggingface dataset:
# Lightweight Anomaly Detection in Digit Recognition Using Federated Learning
## 7 Experimental Results and Analysis
### Federated Learning Scenario 2: IID--Partitioned Dataset
#### 7.5.1 Training on Distinct MNIST Subsets
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{\begin{tabular}{c} **Training** \\ **Federated** \\ **Phase** \\ \end{tabular} } & \multirow{2}{*}{\
\begin{tabular}{c} **Final** \\ **Training** \\ **Loss** \\ \end{tabular} } & \multicolumn{6}{c}{**Anomaly Detection Rate (\%)**} & \multirow{2}{*}{
\begin{tabular}{c} **F1** \\ **Score** \\ \end{tabular} } \\  & & \multirow{2}{*}{**MNIST**} & \multirow{2}{*}{**MNIST-C**} & \multirow{2}{*}{**MNIST-C**} & \multirow{2}{*}{**MNIST-C**} & \multirow{2}{*}{**MNIST-C**} & \multirow{2}{*}{**EMNIST**} & \multirow{2}{*}{**Score**} \\  & & & & & & & & \\ \hline train0 & 0.0098 & 12.65 & 95.25 & 14.90 & 66.85 & 25.25 & 85.25 & 0.8615 \\ fed0 & - & 9.50 & 93.95 & 11.05 & 51.55 & 18.45 & 84.80 & **0.8729** \\ train1 & 0.0051 & 1.70 & 94.45 & 3.85 & 17.10 & 2.55 & 60.15 & 0.7433 \\ fed1 & - & 0.90 & 94.40 & 2.25 & 11.70 & 1.20 & 56.15 & 0.7151 \\ train2 & 0.0037 & 0.80 & 94.45 & 2.40 & 7.60 & 0.95 & 49.35 & 0.6573 \\ fed2 & & 0.50 & 94.50 & 1.40 & 5.55 & 0.50 & 45.70 & 0.6252 \\ train3 & 0.0034 & 0.65 & 94.45 & 1.95 & 5.40 & 0.75 & 45.15 & 0.6193 \\ fed3 & - & 0.50 & 94.40 & 0.95 & 4.90 & 0.50 & 42.55 & 0.5949 \\ train4 & 0.0036 & 0.60 & 94.50 & 1.60 & 5.05 & 0.70 & 43.75 & 0.6062 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Anomaly detection performance for digit 0, Model 3 [196, 64, 196]. Moreover, due to limited memory resources on embedded devices like the ESP32, saving multiple best-performing model checkpoints locally is not feasible. However, this challenge can be addressed by leveraging the central server in the FL setup. Devices can send relevant performance metrics, such as AD rates, to the server, which can then track and store the best global model based on these metrics. At the end of training, the server redistributes the best-performing model back to all devices. This approach enables better model selection aligned with AD goals without increasing memory demands on the devices themselves.
# Multi-Vehicle Tracking Based on Monocular Camera in Driver View
## 3 Proposed Algorithm
### Data Association Module  
The DA is the other part of the TBD paradigm. After the detection results are acquired, the non-maximum suppression (NMS) algorithm is used to find out the best bounding boxes in these detection results.  
As demonstrated in Figure 2, the data association algorithm has two match steps, an embedding match and an IoU match, and a Kalman filter. First of all, the current frame detections and the last frame tracks are matched by an embedding match, which outputs matched tracks, unmatched tracks, and unmatched detections. Then, the last two outputs are matched again with the IoU match. It also has three same format outputs. All of the matched tracks, as well as matched detections, are fed into the Kalman filter. The unmatched tracks will be deleted after 30 frames if they are unmatched in the future matched cycles. The unmatched current frame detections are created as new tracks and put in the inactive tracking pool. The Kalman filter and the matching work together to produce an active tracking pool that outputs the final object bounding boxes with tracked IDs if the condition is met.
## 6 Conclusions  
### Summary of Key Findings  
This review study discussed the Internet of Emergency Services (IoES) as a concept and its potential application in various emergency management scenarios. The study presented five scenarios, namely Disaster Response, Medical Emergency, Public Safety, Smart Transportation, and Industrial Accident, and discussed the possible deployment and application of IoES in these scenarios. The study also highlighted the challenges and limitations associated with the implementation and use of IoES, as well as the risks and concerns associated with its use in emergency management and response. Finally, the study discussed potential future developments of IoES in emergency management and response. The key findings of this study include the potential benefits of IoES in improving emergency response time, coordination, and decision making, the need for addressing technical, legal, and ethical challenges in its implementation, and the importance of considering the privacy and security of personal data in IoES deployment.
# Multipath Routing in Wireless Body Area Sensor Network for Healthcare Monitoring
## 3 Proposed Methodology
### Tracking in \(x\), \(y\) Coordinates  
Tracking tags/nodes by the RFID identifiers is a tricky task. REMR uses the Bayes rule to infer the locations of the tags. The tags' location is described using variable \(x\) and an observation variable assisting coordinate variable \(y\). The joint probability distribution \(q(x,y)\) is computed over both \(x\) and \(y\). In addition, given an observed value \(y\), this joint probability distribution induces the computation of a conditional distribution \(q(x|y)\), which can be used to predict the object's location. More precisely, based on [PERSON] rule, the conditional distribution \(q(x|y)\) can be represented as \[f(x|y)=\ \frac{f(y|x)f(x)}{\int f(y|x)f(x)dx} \tag{4}\]  
Supposing we have n observations \(y^{n}=(y_{1},y_{2},...,y_{n})\) of RFID data at each time-step \(T(x)\), we replace \(f(x|y)\) with  
\[f(y_{1},y_{2},\ldots,y_{n}|x)=\ \prod f(y_{i})=T(x) \tag{5}\]  
Thus, the current state is defined by the posterior density over the random variable location vector \(x\) conditioned on all RFID data:  
\[f(x|y^{n})=\ \frac{f(y^{n}|x)f(x)}{\int f(y^{n})f(x)dx}=\frac{T(x)f(x)}{h_{n}} \tag{6}\]  
where  
\[h_{n}=\ \int T(x)f(x)dx \tag{7}\]  
is the normalizing constant. In this case, one location variable in \(X=(x_{1},...,x_{k})\), e.g., \(x_{1}\), is determined by computing the following marginal posterior density:  
\[f(x_{1}|Y^{n})=\ \int\int\ldots\int f(x_{1},\ \ldots,\ x_{k}|Y^{n})dx_{2} \ldots dx_{k} \tag{8}\]  
These sampling points from function \(f\) in the form of \(x\) and \(y\) show marginal positions of the tags/nodes in the RFID region and are compared with RFID identification memory data for confirmation.
## 5 Conclusions  
In this article, a switching control method was proposed in order to decrease the oscillations of the FOWT in the systems' modes, particularly pitch and top tower fore-aft motions. In order to achieve this goal, the FOWTs motions in different modes were evaluated using RAOs in frequency domain. Through the RAOs' analysis, a new method was proposed for closing/opening the valves of the OWCs housing in the platform. By RAOs, It was observable that for the shorter wave periods (<12.35 s) the open-OWCs-based barge platform has better performance, hence the controller opens the OWCs' valves. On the other hand, for the longer wave periods (>12.35 s), the performance of the the oscillations of the standard barge and closed-OWCs-based barge platforms are better than the open-OWCs-based barge platform. As a result, the controller closes the OWCs' valves in order to effectively reduce the oscillations in pitch and top tower fore-aft motions. In order to measure the incident wave characteristics, an ADCP mounted at the bottom of the barge platform has been considered.  
To show the performance of the proposed control method, a representative scenario was considered as follows; the wave period of 10 s starting from 0-100 s changes to the wave period of 14 s starting from 100-240 s. The results showed that with the wave input period 10 s, the pitch and top tower fore-aft oscillations decreased drastically for the controlled 4 OWC-based barge platform, compared to the standard barge platform. Hence, it has been shown that the proposed control strategy improves the system's performance by reducing undesired oscillations that arise at specific frequencies when compared with the standard uncontrolled barge platform.  
Conceptualization, P.A., F.M., I.M., I.G. and A.J.G.; Formal analysis, P.A., F.M., I.G. and A.J.G.; Investigation, P.A., F.M., I.M., I.G. and A.J.G.; Methodology, P.A., F.M., I.M., I.G. and A.J.G.; Supervision, F.M., I.M., I.G. and A.J.G.; Validation, P.A., F.M., I.M., I.G. and A.J.G.; Visualization, P.A., F.M., I.M., I.G. and A.J.G.; Writing--original draft, P.A., F.M., I.M., I.G. and A.J.G.; Writing--review & editing, P.A., F.M., I.M., I.G. and A.J.G. All authors have read and agreed to the published version of the manuscript.  
This work was supported in part by the Basque Government, through project IT1207-19 and by the MCIU/MINECO through the projects RTI2018-094902-B-C21 and RTI2018-094902-B-C22 (MCIU/AEI/FEDER, UE).  
Not applicable.  
The authors declare no conflict of interest.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} DOF & Degree Of Freedom \\ FAST & Fatigue, Aerodynamics, Structures and Turbulence \\ FFT & Fast Fourier Transfer \\ FOWT & Floating Offshore Wind Turbine \\ RAO & Response Amplitude Operator \\ FRAO & Fore-aft Response Amplitude Operator \\ PRAO & Pitch Response Amplitude Operator \\ OWC & Oscillating Water Column \\ \end{tabular}
## 6 Positioning Accuracy  
The aim of the experiments described in this section is to find out the positioning accuracy of a mobile phone emitting an ultrasound signal calculated using four microphone control points placed just below every ceiling corner (2.83 m high) of a 7 \(\times\)7 m room. The ultrasound signal consists of a very short 21.5 KHz \"beep\". More details on the design of the ultrasound signal used can be found in [30].  
Three experiments were carried out. In Experiment 1 the microphones and the phone were positioned at the same height, 0.12 m below the ceiling. This experiment closely follows the scenario given in [29], where the phone and microphones are all in the same plane. In Experiment 2, the phone was lowered to chest height, putting it roughly 1.6 m below microphone level. No adjustments were made to the trilateration algorithms compared to Experiment 1. During Experiment 3, the phone remained at the same height as in Experiment 2, but a \"room calibration factor\" was introduced into the trilateration algorithms in order to decrease any impact of height difference on accuracy.
# Analyzing Meta-Heuristic Algorithms for Task Scheduling in a Fog-Based IoT Application
## 3 The Proposed Approach
### Hhs
#### 3.2.7 Data Mining  
To find the best low-level heuristic for scheduling, a data mining method is implemented. In this method, first, sample topologies are entered into the fog network as training data; then, scheduling methods are performed on them. Second, a new topology is entered; then, that is scheduled by the best low-level heuristic based on the training phase results. These training and testing phase are as follows.  
**Training phase**: In this part of HHS, as described in Section 3.2, a database is created with some features. Each of the rows of these data has six columns, which include the number of areas, the number of cameras, the energy consumption, the total network usage, the total execution cost, and the type of scheduling algorithm. In fact, these columns include the network topology and output parameters generated by the scheduling algorithms. Since the number of areas and cameras is considered to be between 1 and 4, there are 16 different modes. Additionally, by 4 algorithms, GA, PSO, ACO, and SA create 64 samples and save them to the database.  
**Testing phase**: In this part, according to the network topology (the number of areas and cameras), a quick search is made, and a row of training samples that has the least Euclidean distance as \(\sqrt{(A2-A1)^{2}+(C2-C1)^{2}}\) with the input topology is selected; the last column is run as the scheduling algorithm. A1 and A2 are the number of areas in the training and testing phase, respectively; also, C1 and C2 are the number of cameras in the training and testing phase, respectively, of the proposed method. The result of this equation is used to compare the distance between the input topology in the testing phase and the topology samples in the training database.
# Fast Coherent Video Style Transfer via Flow Errors Reduction
## 3 Methods
### Fast Coherent Video Style Transfer  
#### 3.2.1 System Outline  
Figure 5 shows an overview of the proposed framework. Our method takes the original video frames \(f^{t-2}_{v},f^{t-1}_{v}\),\(f^{t}_{v}\) and per-frame stylized results \(f^{t}_{s}\) and previous output \(\widehat{x}_{out}^{t-1}\) as inputs, and produces coherent output video frames \(\widehat{x}_{out}^{t}\) where \(t\in\{1,\dots,N\}\) and N denotes the total number of frames. We develop a mask generation method consisting of the set of techniques mentioned in Section 3.1 to reduce the flow errors, and an initialization generation method to output a new initial image which is much closer to the final coherent result which speeds up the gradient-based optimization method. Specifically, starting with original the video frames\(f^{t-2}_{v}\), \(f^{t-1}_{v}\) and \(f^{t}_{v}\) in time step \(t\), our method generates a backward flow \(F^{t}\) from time \(t\) to \(t-1\) using Flownet2 [63], an image \(w^{t}\) that warps previous output image \(\widehat{x}_{out}^{t-1}\) and \(F^{t}\) and a mask \(M^{t}\). Then we compose these three images to obtain an initial image \(\widehat{x}_{init}^{t}\) which is fed into the network along with the three images above. The optimization process needs much less iterations than previous methods (e.g., [23; 24]) as flow errors have been reduced significantly in the initial image. In order to obtain a long-term coherency, we adopt a recurrent strategy so that the output result \(\widehat{x}_{out}^{t}\) will be fed as input into the next time step. Figure 6 shows the recurrent strategy. The short-term coherency between adjacent outputs is propagated into a long-term temporal consistency during the entire video style transfer process. In this way, our method is capable of propagating all the flow traceable points as far as possible. The details of mask generation, initialization generation and gradient-based optimization network will be discussed in following subsections.  
Figure 5: System overview. Starting from three consecutive frames, our system takes corresponding per-frame stylized \(f_{s}^{t}\), mask \(M^{t}\) and warped image \(w^{t}\) as inputs, then computes initialization \(\hat{x}_{init}^{t}\) for gradient-based optimization video stabilization network.  
Figure 6: Recurrent strategy for video style transfer.
## 2 Materials and Methods
### Spray Deposition  
#### 2.3.1 Crop Characteristics  
Two cultivars, namely Maestro(r) and Soprano(r) (Vilmorin, Paris, France), were selected to represent two different crop structures with either a spread or upright canopy configuration, respectively. The carrots were seeded (early-stage carrots-BBCH15-5 leaves) or planted (full-grown carrots-BBCH 49, ready to harvest) in bins of 1.0 m \(\times\) 1.2 m, according to the plantation structure of 3 lines \(\times\) 3 rows per bed, with 0.4 m between rows, and 50-60 mm between lines, as described in [20].  
This plantation scheme resulted in approximately 150 plants per m\({}^{2}\), representing a realistic crop density of 1.5 million plants per hectare. The bins were filled with a breeding ground up to 0.16 m from the top of the bins. The full-grown carrots came directly from a carrot growing estate (Planete Vegetale estate, Cestas, France) and were replanted in 6 bins (3 lines \(\times\) 3 rows, Figure 2a) the day after harvest. The deposition measurements were performed the next days. Carrots for the early-stage experiment were seeded in 3 bins (3 lines \(\times\) 2 rows, Figure 2b) at INRAE Montpellier facility in September 2019 and used for experiments three months later. Five plants were defoliated, and leaves were scanned to determine the leaf area index. Average LAI values of 2.3 \(\pm\) 0.4 and 4.6 \(\pm\) 1.2 were found for the early-stage and full-grown carrots, respectively.  
\begin{table}
\begin{tabular}{l c c c} \hline  & **Spray Configuration a** & **Temperature (c**C**) & **Relative Humidity (\%)** \\  & & **Mean \(\pm\) s.d.** & **Mean \(\pm\) s.d.** \\ \hline  & XR 110 04 & 18.4 \(\pm\) 2.9 & 81.0 \(\pm\) 12.7 \\ Deposition & AI 80 04 & 15.5 \(\pm\) 0.5 & 51.0 \(\pm\) 2.8 \\ Full-grown carrots & XR 80 04 & 18.0 \(\pm\) 1.1 & 68.8 \(\pm\) 5.9 \\  & XR 80 02 & 11.6 \(\pm\) 1.4 & 87.5 \(\pm\) 7.9 \\  & AIUB 85 04/AI 110 04 & 10.2 \(\pm\) 1.5 & 68.3 \(\pm\) 6.8 \\  & XR 110 04 & 13.1 \(\pm\) 0.2 & 53.0 \(\pm\) 1.0 \\  & AI 80 04 & 13.5 \(\pm\) 0.3 & 51.7 \(\pm\) 2.5 \\ Deposition & XR 80 04 & 14.2 \(\pm\) 0.6 & 46.5 \(\pm\) 3.8 \\ Early-stage carrots & XR 80 02 & 17.9 \(\pm\) 0.8 & 53.5 \(\pm\) 2.8 \\  & AIUB 85 04/AI 110 04 & 21.2 \(\pm\) 1.5 & 46.2 \(\pm\) 4.1 \\  & XR 110 04 & 16.7 \(\pm\) 0.1 & 93.0 \(\pm\) 0.1 \\  & AI 80 04 & 17.1 \(\pm\) 0.2 & 80.5 \(\pm\) 3.5 \\ Spray Drift Potential & XR 80 04 & 19.7 \(\pm\) 0.3 & 65.5 \(\pm\) 3.8 \\  & XR 80 02 & 16.2 \(\pm\) 0.2 & 92.3 \(\pm\) 1.2 \\  & AIUB 85 04/AI 110 04 & 18.7 \(\pm\) 0.6 & 68.3 \(\pm\) 4.7 \\ \hline \end{tabular} a  
\end{table}
Table 2: Meteorological conditions during deposition and spray drift potential measurements.
# Study on Frost Heave and Thaw Settlement Characteristics of Sanya Estuary Channel Soil Layer
## 5 Discussion  
This study conducted a comprehensive analysis of the frost heave and thaw settlement characteristics of typical soil layers in the Sanya River Estuary Tunnel Project, with a focus on the impact of different moisture contents on the frost heave and thaw settlement performance. Through systematic freeze-thaw experiments on soil layers such as fine sand, silty clay, gravel sand, and clay, it was found that the frost heave rate and thaw settlement coefficient of silty clay exhibited a significant linear relationship under varying moisture contents. Particularly at higher moisture contents, both the frost heave rate and thaw settlement coefficient increased, posing greater threats to the structural safety.  
This finding has important implications for the safe construction of tunnel projects. The pipe curtain freezing method employed in tunnel construction may trigger serious frost heave and thaw settlement issues, especially in soil layers with high moisture content. Therefore, by controlling the moisture content of the soil, the risks associated with frost heave and thaw settlement can be effectively reduced, thus ensuring the safety and stability of the construction.  
Moreover, given that silty clay exhibits the most adverse frost heave and thaw settlement characteristics in the Sanya River Estuary Tunnel Project, this paper suggests that future research could consider soil improvement techniques, such as the use of additives like cement for soil stabilization. This measure could not only enhance the engineering properties of the soil but also further reduce the structural risks caused by frost heave and thaw settlement.
# Mechanical Characterisation and Modelling of Elastomeric Shockpads
## 5 Discussion  
Microfoam models provided a good fit to the experimental shockpad data, particularly for the Berleburger shockpad. Although the Holywell shockpad exhibited more traditional hyper-elastic and viscoelastic behaviour, the microfoam model performed better overall than a more traditional hyper-elastic and viscoelastic Bergstrom-Boyce model. Further work is needed to understand the reasons for the differences in loading response for the two shockpads. Using the AAA test device to experimentally capture the stress-strain behaviour of the two shockpads under high strain rates was successful in helping to define these material models. Although the predictive capabilities of the two models have not been addressed, the ability of the models to fit not only to the impact drop height on which they were based, but also to lower energy impact drop heights, has been demonstrated. In reality, shockpads are exposed to a wide range of impact energies and a model that can estimate the response over this full range through a single set of measurements is relevant in optimising the design of 3G turf surface systems. The AAA test can be very limited in terms of output if only considering the shock absorption and vertical deformation values as standalone measures of surface characteristics. Extracting the raw data from each of the drops allowed for a deeper analysis to be completed. Calculation of the corresponding stress and strain not only provided more information about the whole material response but also a way to use the data for material model calibration. This proved key in creating material models for both shockpads as using data recorded at slower strain rates, such as in the study by [PERSON] et al. (2010), did not yield a material model that could successfully be used within AAA simulations. The three regions of shockpad deformation, as described in the literature ([PERSON], 1999; [PERSON], 1999), were observed in the experimental stress-strain response at higher drop heights. An initial period of low stiffness and high deflection was followed by a transition region and finally an increase in stiffness at higher strains as the air was squeezed out of the system and rubber-on-rubber contact ensued. At lower drop heights this was less evident and the response was more linear in nature as the shockpad did not experience sufficient strain to enter the second or third stages of compressive behaviour. Interestingly, the loading response for the Berleburger shockpad showed undulations in the loading curve from the AAA impact that was not seen in either the unloading, or in the Holywell shockpad. Repeatability between drops of the same height remained high as the shockpads remained in their elastic region even at the highest drop heights. Thus, a single drop could be used to represent the shockpad response. The Berleburger shockpad was stiffer and had higher peak accelerations
# Limited-Angle Computer Tomography with Truncated Projection Artifacts Removal
## 4 Discussion  
Both experiments with simulated data and human breast data demonstrate that our algorithm has a better TPA removal effect and can effectively restore tissue obscured by TPA. Since the diffusion method uses the updated value of the partial area to update the voxels of entire breast images, errors will inevitably occur during the diffusion process. Although these errors will gradually decrease in the iterative process [10], they will still influence the final reconstructed image. Additionally, as the height of the breast slice increases, the un-updated area gradually expands and enlarges the errors caused by the diffusion method in high-level slices [15; 16]. In contrast, our algorithm completes the truncation of the original projected data, and each update includes all voxels when we reconstruct the completed data, thereby eliminating TPA thoroughly. Furthermore, with the property of prior knowledge independent, our method can be applied in various environments without the need to adjust the parameters in the image processing.