esa-sceva/satcom-chunk-collection huggingface dataset:
# An FSFS-Net Method for Occluded and Aggregated Fish Segmentation from Fish School Feeding Images
## 4 Experiments
### Comparison with the State-of-the-Art
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & **Backbone** & **Image Size** & **MIoU (\%)** \\ \hline LinkNet [51] & ResNet18 & 704 \(\times\) 1280 & 59.06 \\ ENet [52] & — & 720 \(\times\) 1280 & 65.03 \\ BiSeNet v2 [47] & — & 720 \(\times\) 1280 & 72.49 \\ DDRNet [50] & 39 & 704 \(\times\) 1280 & 75.68 \\ \hline OCNet [43] & ResNet101 & 360 \(\times\) 480 & 42.39 \\ DANet [42] & ResNet101 & 360 \(\times\) 480 & 43.29 \\ FCN-8s [53] & VGG16 & 352 \(\times\) 480 & 69.44 \\ SegNet [36] & VGG16 & 360 \(\times\) 480 & 69.52 \\ DFN [41] & ResNet101 & 352 \(\times\) 480 & 69.77 \\ DFN [41] & ResNet50 & 352 \(\times\) 480 & 55.26 \\ ExFuse [54] & ResNet50 & 352 \(\times\) 480 & 70.46 \\ ExFuse [54] & ResNet101 & 352 \(\times\) 480 & 70.05 \\ PSNet [38] & ResNet50 & 473 \(\times\) 473 & 71.18 \\ HRNet v2 [55] & V48 & 352 \(\times\) 480 & 75.36 \\ DeepLab v3 plus [39] & VGG16 & 360 \(\times\) 480 & 75.83 \\ GCN [49] & VGG16 & 352 \(\times\) 480 & 75.81 \\ GCN [49] & ResNet152 & 352 \(\times\) 480 & 76.14 \\ U-Net [37] & — & 360 \(\times\) 480 & 77.98 \\
**SPSA-Net (ours)** & — & 360 \(\times\) 480 & **79.62** \\ \hline \hline \end{tabular}
\end{table} 
Table 2: MIoU score comparison with other methods on fish feeding behavior dataset. Our proposed method achieves state-of-the-art performance. only three categories (contains background category). Therefore, the method we proposed is effective in segmenting fish school images.
## 3 The Proposed Pattern Recognition Method
### The Optimal PF Component Selection Based on Kullback-Leibler Divergence  
After LMD decomposition, several PF components can be obtained. However, it is unnecessary to conduct the feature extraction for all PFs. In order to choose an optimal PF component (OPF), which contains the most feature information, the Kullback-Leibler divergence (KLD) is taken as the criterion, to measure the relevancy between each PF and the original signal. The main steps of OPF selection with KLD can be described as follows:  
1. Suppose \(p(x)\) and \(q(x)\) are the probability density functions of original signal \(x(t)\) and \(PF_{i}(t)\), respectively. \(p(x)\) can be defined as follows: \[p(x)=\frac{1}{Nh}\sum_{i=1}^{N}K(\frac{x_{i}-x}{h}),\] (16) where \(N\) denotes the sampling number of \(x(t)\), \(h\) is called the window width or smoothing parameter, and \(K(*)\) denotes a kernel function and is commonly expressed as: \[K(t)=\frac{1}{\sqrt{2\pi}}e^{-t^{2}/2},\] (17) Likewise, the probability density function \(q(x)\) can be obtained.
2. The Kullback-Leibler distance between \(x(t)\) and the \(i\)th \(PF\) can be defined as: \[\delta_{i}(p,q)=\sum p(x)\log\frac{p(x)}{q(x)},\delta_{i}(q,p)=\sum q(x)\log \frac{q(x)}{p(x)},\] (18)
3. The KLD between \(x(t)\) and the \(i\)th \(PF\) can be calculated as: \[D_{i}=\delta_{i}(p,q)+\delta_{i}(q,p),\] (19) The normalized KLD values can be obtained by Equation (20). \[\hat{D}_{i}=D_{i}/\sum_{i=1}^{k}D_{i},\] (20) The smaller the KLD value is, the closer the correlation between the PF and original signal is, and vice versa.
# Feature Selection from Lyme Disease Patient Survey Using Machine Learning
## 1 Introduction
** There are many approaches for feature selection, where one seeks to identify the most salient features for a predictive model. These are roughly divided into wrapper and filter methods; wrapper methods evaluate subsets of features in a predictive model, while filter methods select features without information from the predictive model [11]. The feature selection approaches we apply are simple wrapper approaches. We also compare to results of popular supervised feature extraction approaches, which aim to build a small number of features that capture the most salient data information for the predictive model [12]. We compare to the results produced by semi-supervised nonnegative matrix factorization [13] and supervised principal component analysis [14]. Application of feature selection and feature extraction techniques to biological and medical data to yield interpretable predictive models has a long history; see, e.g., [15; 16; 17; 18; 19]. **Organization. ** In Section 2, we describe the MyLymeData dataset and preprocessing steps, and introduce the techniques and models that we use throughout the paper. In Section 3.1, we run all the models on the full MyLymeData dataset to evaluate the potential of each model to predict GROC labels using all of the data features. In Section 3.2, we apply the models to individual features to identify the features that are most important in predicting GROC labels and we form a subset of top features by aggregating the results of all the models. In Section 3.3, we evaluate the predictive ability of this subset of top features in comparison to the full dataset. Finally, in Section 4, we discuss our results and their implication to Lyme Disease treatment protocols.
## 1 Introduction
As the initial proof-of-concept approach in Reference [13] did not scale well to larger application scenarios, we described a more advanced cooperative optimization approach relying on matrix factorization in a preliminary conference paper [16]. The current article deepens and extends this work, particularly in the following aspects. We now apply a more advanced matrix factorization originally introduced in Reference [17], which allows us to exploit that only a small fraction of locations is actually relevant for each user and that user data is not missing at random. Moreover, the COA in Reference [16] considered only mobility applications in which single service points are sufficient for satisfying a particular demand of a user, such as the placement of charging stations for electric vehicles. The main contribution of this paper is to extend the solution approach towards applications in which the satisfaction of demand typically relies on the existence of suitable pairs or, more generally, tuples of service stations, such as in the case of bike or car sharing systems where a vehicle is first picked up at a rental station near the origin and returned at a station within easy reach of the destination. This extended framework is tested on different artificial benchmark scenarios using an idealized simulation of user interactions. The benchmark scenarios were hereby created in a controlled way so that optimal reference solutions could be obtained for comparison. One group of benchmark scenarios was derived from real world taxi trip data from Manhattan. To characterize the amount of user interaction COA requires, an upper bound on the number of necessary non-redundant user interactions for obtaining full knowledge of user preferences is derived as a baseline. Results on our instances show that solutions generated by COA feature optimality gaps of only 1.45%, on average, with 50% of performed user interactions. The next section discusses related work. In Section 3, we introduce our formal problem setting, the _Generalized Service Point Distribution Problem (GSPDP)_, which reflects the essence of various location problems for mobility services. Afterwards, in Section 4, we detail the cooperative optimization framework for solving the GSPDP. Section 5 describes how benchmark scenarios for testing were generated, and, in Section 6, we present and discuss experimental results. Section 7 concludes this work with an outlook on promising future work.
# Smart Grid Architecture for Rural Distribution Networks: Application to a Spanish Pilot Network
## 2 Typical Rural Distribution Networks
### A Particular Pilot Network in a Rural Area  
The SRG network is focused on a real rural distribution grid with a substantial potential for improving efficiency, in particular in terms of continuity of supply [34]. The distribution network where the project is carried out is the last part of a 5 kV network in a rural zone operated by EyPESA (Distribution System Operator, Granollers, Spain) [37]. EyPESA is a DSO that operates a distribution system in Catalonia (Spain). EyPESA provides service to around 56,000 customers along its 1,500 km of lines [37]. The principal particularity of the EyPESA system is that about 50% of its network is deployed in a rural environment where over two-thirds of the customers are from the domestic and service sectors. The EyPESA network is connected to the transmission system at 220 kV through two fully automated primary substations. Internally, the DSO distributes the electricity at 40 kV and then to networks at 20 kV, 5 kV and 3 kV, through automated primary substations. Finally, the DSO distributes to its clients using LV networks through Secondary Substations (SSs) that are mostly not automated.  
The network chosen here concerns an area with a low population density, barely 25 customers distributed across four low-level secondary substations (see Figure 1) and who are residential and agrarian. This network is characterized as a non-manageable radial grid, where operational safety is guaranteed--as in traditional networks--through manual switch-disconnectors and fuses. Furthermore, failure detection and access is complicated by the fact that the MV lines of the grid cross valleys and mountains, exposing them to adverse weather conditions.  
As seen in the right-upper part of Figure 1, the electrical scheme of the PN is depicted. An overhead bare line comes from a substation which interconnects to a 40 kV subtransmission system located several thousand meters from the PN. In the PN, the overhead bare line covers from several hundred to a few thousand meters, constituting the MV network at 5 kV. In that network, three manual switch-disconnectors can be tripped during maintenance tasks (see Figure 1). The MV lines end at the three-phase circuit breakers of each SS. The three-phase circuit breakers are directly connected to the SS transformer. Following the SS transformer are the LV lines, which are four-wire braided cables that are protected by single-phase fuses, providing passive protection in case of overcurrent. Finally, the SS LV networks supply customers dispersed around the rural area. Each of them is equipped with a smart meter and a power switch that limits maximum power consumption and provides protection against grid eventualities (though it does not actively manage the load). Most of the clients in the PN demand a single-phase power supply, and this is translated into an unbalanced load through the three-phase distribution network.
## 3 Results: Publication Analysis
### Relationships between Sense of Presence and the Study Variables
#### 3.4.3 Relationships between Sense of Presence and Conditions (Technology and User Profile)
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Ref.** & \multicolumn{2}{c}{**Categories**} & \multicolumn{2}{c}{**Human**} & \multicolumn{2}{c}{**HR Behavior**} \\ \hline  & \multicolumn{2}{c}{**Conditions with Results**} & \multicolumn{2}{c}{**Presence**} & \multicolumn{2}{c}{**HR is (\(<\) / \(\equiv\) / \(>\)) in (A) than in (B)**} \\ \hline  & \multicolumn{2}{c}{**Conditions with Effects on HR**} & \multicolumn{2}{c}{**Dimension**} & \multicolumn{2}{c}{**HR is (\(<\) / \(\equiv\) / \(>\)) in (A) than in (B)**} \\ \hline
[23] & Condition & Tech, & Hardware (Resolution) & Presence & (Low resolution) & \(\equiv\) & (Control) \\ \hline
[31] & Condition & Tech, & Support (Audio Channels) & Presence & (Stereo) 1 & \(>\) & (Mono) \\ \hline
[59] & Condition & Tech, & Support (Audio Channels) & Presence & (Triple) & \(>\) & (Mono) \\  & Condition & Tech, & Support (Audio Spatialization) & Presence & (Spatialized) & \(>\) & (Anechoic) \\ \hline
[9] & Condition & Tech, & Support (Audio Signalization) & Presence & (Spatialized) & \(>\) & (Control), (Non-spatialized) \\ \hline
[60] & Condition & Tech, & Support (Audio Channels) & Presence & (6 Speakers) & \(>\) & (Mono), (Stereo) \\ \hline  & & & Presence & M & (HMD) + (Control) & \(>\) & (PC) + (Control) \\  & & & Presence & M & (VideoWall) + & \(>\) & (PC) + (Control) \\
[1] & Scenario & MIP, & Sadness Support (Display) & Presence & M & (PC) + (Sadness) & \(>\) & (HMD) + (Sadness) \\  & Condition & Tech, & Support (Display) & Presence & M & (VideoWall) + & \(>\) & (HMD) + (Sadness) \\  & & & Presence & M & (Sadness) & \(\equiv\) & (PC) + (Sadness) \\ \hline
[12] & Condition & Tech, & Support (Display) & Spatial Presence & & (VR) & \(>\) & (Video) \\  & Condition & Tech, & Hardware (Graphic Realism) & Presence & & (High realism) & \(>\) & (Low realism) \\ \hline  & & & Presence & M & (Center) + (CoP & \(>\) & (Right) + (CoP Front) \\  & & & Presence & M & (Zoom) + (CoP Front) & \(>\) & (Right) + (CoP Front) \\  & & & Presence & M & (Center) + (CoP & \(>\) & (Left) + (CoP Front) \\  & & & Presence & M & (Zoom) + (CoP Front) & \(>\) & (Left) + (CoP Front) \\
[19] & Condition & Tech, & Hardware (PoV) & Presence & M & (Zoom) + (CoP Front) & \(>\) & (Left) + (CoP Front) \\ Condition & Tech, & Hardware (CoP) & Presence & M & (Right) + (CoP & \(>\) & (Center) + (CoP ) \\ Condition & Tech, & Support (Display) & Presence & M & (Left) + (CoP Center) & \(>\) & (Center) + (CoP ) \\  & & & Presence & M & (Right) + (CoP & \(>\) & (Zoom Out) + (CoP ) \\  & & & Presence & M & (Left) + (CoP Center) & \(>\) & (Zoom Out) + (CoP ) \\ \hline  & & & & & & & \\ \end{tabular} Note. 1 This result is indicated as a trend by the authors. M: Moderator effect; PoV: Point of View; CoP: Center of Projection.  
\end{table} 
Table 15: Effects of conditions (technology and user profile) on the sense of presence indicators.
# Transmission Reduction for UV and IR Radiation with Dyed Lyocell Knitted Textiles
## 4 Discussion
Finally, coatings from sol-gel material and indium tin oxide applied on cotton can lead to the low transmission for near-infrared light with transmission values of 10% or less [73]. These sol-gel composite materials are superior compared to the developed Lyocell knitted materials if the capability for protection against IR light is discussed. However, their UV protective properties are not that excellent, and also a change in textile properties as flexibility and hand feeling occurs after sol-gel application. By view of these comparative materials, it can be stated that the currently developed dyed Lyocell materials exhibits comparable or even better protection against UV light and near-infrared light. Especially advantageous is the remaining textile character of the Lyocell knitted fabrics which enables direct transfer to application in the textile and clothing sector.
# Vehicle Rollover Warning and Control Based on Attitude Detection and Fuzzy PID
## 3 Selection of the Control Strategy
### Lower Actuators
#### 3.2.2 Overall Design of the Wire-Controlled Auxiliary Braking System  
The overall design of the vehicle wire control actuation system is based on the original hydraulic braking system of a forest fire truck. The design adopts an EHB system, which includes two major parts: an electronic control system and an actuator. The electronic control system includes all types of sensors and their signal input unit, data acquisition and processing unit, control decision unit, and actuator drive unit, which uses an electric booster instead of a vacuum booster. A hydraulic system, including a motor, hydraulic pump, high-pressure accumulator, oil tank, brake master cylinder, oil filter, high-speed switch solenoid valve, and brake wheel cylinder, was used. The general design of the linear control actuator system is illustrated in Figure 13.  
While driving the vehicle, the inertial guidance system and all types of sensors input the real-time lateral angular speed, lateral acceleration, brake pedal stroke, and other signals of the vehicle into the data acquisition and processing unit of the electronic control system. Subsequently, the real-time LTR and other values of the car are input to the control decision unit, which decides whether the vehicle has braking demand according to the selected control strategy, such as ABS, ESP, anti-rollover control, etc., and calculates the target brake wheel cylinder pressure. It then issues the braking command to the actuator; produces outputs including the corresponding motor signal and high-speed switching solenoid valve pulse-width modulation signal; controls the target brake wheel cylinder to reach the required pressure; and adjusts the control command according to the wheel cylinder pressure, wheel speed, lateral acceleration, and other dynamic feedback signals in time to realize braking control.
# Micro-FL: A Fault-Tolerant Scalable Microservice-Based Platform for Federated Learning
## 4 Micro-FL
### Micro-FL Workflow  
The following subsection provides an overview of the workflow of the Micro-FL system. The process is characterized by a series of steps that ensure the smooth execution of federated learning tasks. Each of these steps is explained in detail below:  
Clients initiate a registration process with Micro-FL via a dedicated web application interface. Based on the number of clients registered and prepared to contribute to the federated learning process, the Micro-FL administrator notifies registered clients ready to contribute, signaling the start of a new training iteration. The Aggregator service actively monitors connected clients and their statuses. When a certain number of participating clients is reached, it triggers the initialization of a model, which is subsequently disseminated to all the clients. Clients continuously listen for updates from the aggregator service. Upon receipt of the model from the aggregator service, they start training on their local datasets. After training, the clients transmit their model parameters to the server through the communication service. All messages submitted by clients are transmitted securely through the communication service and are logged into the database. The aggregator microservice continuously monitors the client messages during each iteration. When the number of messages is equal to the total number of clients, the aggregator synthesizes a new global model using the individual client models. The Aggregator service dispatches a fresh message to the clients, and the cycle from steps to repeats. This iterative process continues and is monitored until a specified number of iterations are completed or a pre-defined model performance metric is achieved.
# Optimization of the Battery Pack Heat Dissipation Structure of a Battery-Type Loader
## 2 Heat Dissipation Structure Design  
Due to the limited installation space reserved for the battery pack in a downhole loader (located at position 1 in Figure 1 below), the volume of the battery pack is constrained in that it cannot be too large or too small without affecting heat dissipation. Considering the volume of this battery pack, a conventional serpentine airflow channel was used inside to ensure that it would have good ventilation conditions, and the battery pack structure was designed in advance. The battery pack heat dissipation structure and parameters are shown in Figure 1 and Table 1 below.  
To simplify the simulation model, the effect of the thickness of the bulkhead inside the pack on the heat dissipation effect of the battery pack was ignored. Inside the battery pack, air enters from the first layer at the bottom and exits from the third layer at the top, which creates a serpentine flow of air inside the battery pack. To facilitate modeling and simulation analysis, the influences of the controller, wiring, and mounting structure inside the battery pack on battery heat dissipation were omitted.
## 4 Future Research Directions
### Scheduling  
Sensing tasks should be allocated and processed among sensors in a minimum time, so that users can draw prompt and effective conclusions through analyzing the sensed data and can reduce power consumption to prolong the lifetime. Scheduling tasks in WSNs is one of the most challenging problems.  
To remove the potentially selfish behavior of the sensors, a non-cooperative game algorithm for task scheduling in WSNs was proposed in [55]. In the proposed algorithm, according to the Divisible Load Theory, the load are divided into any-size parts, and they are distributed reasonably to every node from the sink on the basis of the processing and communication capability. By removing the performance degradation caused by communications interference and efficiency lost due to idle states, the decreased load completion time and the improved network resource utilization are achieved. Strategyproof mechanism can provide incentives to the sensors in order to force them to obey the prescribed algorithms, and to accurately report their parameters, it will lead to the demand for a more efficient task scheduling. A utility function relating the total task completion time, and tasks allocating scheme was then designed where the NE of the game algorithm was proved. The simulation results had shown that in a mechanism, selfish nodes can voluntarily report their true processing capability and try their best to participate in the mechanism, thereby, the total time for accomplishing the task is minimized and the energy-consuming of the nodes is balanced.
## 8 Results
### Coexistence with Wireless Power Transfer  
To evaluate the concept of the low-interference coil design, the mutual inductance of energy and data coils of the same substrate were measured with the method described in Section 8.1. The acquired values at the operational frequency of the power transfer link at 40.68 MHz are \(M_{13}=2.1\) nH and \(M_{24}=2.8\) nH, being approximately as large as the data coils' mutual inductance at a distance of 15 mm. The resulting coupling factors \(k_{ij}=M_{ij}/\sqrt{L_{i}L_{j}}\) are \(k_{13}\approx 1.6\times 10^{-3}\) and \(k_{24}\approx 1.6\times 10^{-2}\), so that the on-substrate coils are subject to weak coupling.  
The isolation of energy and data coil of different devices, exemplarily determined for the primary energy coil and the secondary data coil, was also analyzed by acquiring the ratio of mutual inductances: The measure of interference \(M_{14}(40.68\) MHz) was divided by the measure of data link coupling \(M_{34}(13.56\) MHz). As capacitive and resonant effects might be more pronounced for a lossy dielectric environment and the higher operational frequency of the energy link (40.68 MHz), the cross-coupling analysis was performed in pork muscle tissue. The results are shown in Figure 24: For axial alignment, \(M_{14}\) is more than 10 times smaller than \(M_{34}\), which validates the operational principle of the butterfly-shaped coils even in lossy dielectric media.  
Moreover, the voltage transfer function of the matching network was derived from a two-port S-parameter measurement. The result of Figure 25 indicates a suppression of the energy carrier by 50 dB with respect to the data carrier, providing additional isolation.  
Finally, the bit error rate resulting from the combined effect of butterfly-shaped coils and filter network was experimentally determined for a data coil separation distance of 10 mm: A class E driver stage was driving the primary energy coil with a 40.68 MHz energy carrier at a power level of 100 mW. Data were sent from the secondary data coil to the primary data coil, so that the energy transmitter was in close proximity to the data receiver. With the methods of Section 8.5, a bit error rate of \(3\times 10^{-5}\) was measured, which is one order of magnitude higher than the result of the interference-free operation (cf. Figure 22), but still tolerable in a practical implementation.  
Figure 24: Cross-coupling analysis of data and energy coils in pork tissue environment: The ratio of \(M_{14}(40.68\) MHz) and \(M_{34}(13.56\) MHz) is shown versus coil distance \(d\) and lateral misalignment \(l\), the measurement values on a grid with 5 mm resolution are shown as a numerical reference.