esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Discussion  
### About the Proposed ASGASN Model  
In recent years, there have been breakthroughs in deep learning architectures for semantic annotation of high-resolution images, which provide a completely new way of thinking for high-precision extraction of buildings. Some of these advanced FCN-based models (e.g., USPP [2], ARC-Net [4], and MC-FCN [47]) have improved the learning ability of image features and have better classification results. However, the contours extracted by existing networks are not accurate enough in the face of identical objects of various shapes, textures and sizes in high-resolution remote sensing images.  
In this paper, we designed a new generative adversarial network with skip connections and ASPP for semantic segmentation, called ASGASN. The model has three key innovations. (1) The main contribution of this study is to apply the self-adversarial property of GAN to the automatic segmentation of buildings and develop a new network named ASGASN. While the more common segmentation networks are mainly based on the FCN architecture, which mainly controls the training of the network through the loss function and lacks the self-optimization of the network. We can intuitively see from the content of the red box in Figure 11 that the adversarial learning feature of ASGASN can extract some buildings that cannot be recognized by the FCN framework.(2) In order to better obtain the detailed features of the building outline, the segmentation network of the architecture uses a larger convolutional kernel and global convolutional block to expand the semantic information receiving domain, and the classical combination of ASPP+ skip connection is added to consider the contextual semantic information, which effectively improves the sensitivity of the model to the building. (3) To improve the discriminator network's ability to discriminate true or false, the multi-level downsampled feature maps are fused and ultimately guide the training of the entire ASGASN. Through these three innovations, ASGASN achieves a good performance improvement and can extract different types of building contours more accurately. The OA of the ASGASN on the WHU and China typical city building datasets are 97.4% and 92.6%, respectively, with F1-scores of 94.4% and 91.1%, respectively, and IoU values of 89.4% and 83.6%, respectively. Moreover, these results demonstrate the practical performance of ASGASN in real-world application scenarios.
# Comparison of Countermovement and Preferred-Style Jump Biomechanics in Male Basketball Players
###### Abstract  
The purpose of this study was to investigate and compare the biomechanics of countermovement (CMJ) and preferred-style (Pref) jumps. Eight male basketball players (age: 19 \(\pm\) 1 year; height: 1.84 \(\pm\) 0.14 m; mass: 92.8 \(\pm\) 11.4 kg) participated in a cross-sectional study for which they performed max effort CMJ and Pref) while motion capture and force plate data were recorded. The CMJ were performed according to common procedures. For the Pref), the eight players chose to use a short approach run and a step-in jump, with a clear lead and trail leg foot contact pattern. Vertical ground reaction forces (GRF), center-of-mass (COM) parameters, as well as hip, knee, and ankle flexion angles, extension velocities, net joint moments, powers, and work were all calculated and used for analysis. Bi-lateral data from the CMJ were averaged, whereas lead and trail leg data from the Pref were kept separated. The Pref was characterized by greater jump height and GRF and shorter contact times. Joint-level differences indicated that the Pref) was characterized by larger joint kinetics. Importantly, very few biomechanical variables of the CMJ and Pref) were correlated, which suggests that each jump type is characterized by unique movement strategies. Since Pref) may better represent athlete- and sport-specific movement pattern, these findings could have implications for assessing and monitoring neuromuscular performance of basketball players.  
sports; biomechanics; performance; movement strategy +
Footnote †: journal: Applied Sciences
# An Optimal Online Resource Allocation Algorithm for Energy Harvesting Body Area Networks
## 4 Resource Allocation Algorithm  
### Lyapunov Optimization  
Laypunov optimization theory introduces a method to control the utility problem. We adopt Laypunov optimization theory to decompose the objective into optimization goals for each time slot. We define \(\Theta(t)=(I(t),J(t))\) as the network state in time slot \(t\) that represents the length of data queueand energy queue. We define a Laypunov function, \(L(t)\), as the sum of squares of backlogs in the data queues and the spare capacity in sensors' batteries.  
\[L(t)=\frac{1}{2}\sum_{n\in N}(I_{n}(t))^{2}+\frac{1}{2}\sum_{n\in N}(-\widehat{I }_{n}(t))^{2}, \tag{10}\]  
where \(\widehat{I}_{n}(t)=\Omega-J_{n}(t)\) denotes the spare capacity battery of sensor \(n\). Laypunov function \(L(t)\) is a scalar to measure the occupancy of data queue and energy queue. A small value of Laypunov function \(L(t)\) means a small length of data queue and a full battery of capacity. According to the characteristics of the data queue and energy queue, \(L(t)\) is time coupled. We define the conditional Lyapunov drift, denoted by \(\Delta(t)=\mathbb{E}[L(t+1)-L(t)|\Theta(t)]\) for converting time-coupled problem to non-time-coupled problem. Our goal is to minimize \(\Delta(t)\) in each slot time. The data queue is pushed towards zero to stabilize the data queue. The energy queue is pushed towards their battery capacity \(\Omega\) to guarantee having enough energy for data collection and data transmission.  
The maximum user utility has not yet been incorporated in Laypunov function \(L(t)\). We include a weighted version of user utility into the Lyapunov drift. We use the \(\Delta_{V}(t)=\mathbb{E}[\Delta(t)-VO(t)|\Theta(t)]\), to substitute the \(\Delta(t)\), where \(V\) is a weight, which means how much we emphasize on user utility. Minimization of \(\Delta_{V}(t)\) can minimize the \(\Delta(t)\). By minimizing \(\Delta_{V}(t)\), we can achieve two goals: (1) stabilizing the BAN system; and (2) maximizing the user utility. The drift-minus-utility \(\Delta_{V}(t)\) is a quadratic function of the queue lengths and variables in \(Z(t)\), and we can derive the upper bound of \(\Delta_{V}(t)\). Minimization of \(\Delta_{V}(t)\) by minimizing its upper bound.  
Theorem 4.1: _Given the network state, the upper bound of \(\Delta_{V}(t)\) is:_  
\[\Delta_{V}(t)\leq B+E[D_{V}(t)|\Theta(t)], \tag{11}\]  
_where \(B\) is a finite constant and independent of \(V\), whose expression is following:_  
\[B=\frac{2}{N}[(c^{max})^{2}+(r^{max})^{2}+(h^{max})^{2}+(a^{max})^{2}], \tag{12}\]  
_and \(D_{V}(t)\) is expressed as_  
\[\begin{split} D_{V}(t)&=\sum_{n\in N}\left[-\widehat {I}_{n}(t)h_{n}(t)\right]+\sum_{n\in N}\left[a\widehat{J}_{n}(t)c_{n}(t)+I_{n} (t)c_{n}(t)-VU(c_{n}(t))\right]\\ &+\sum_{n\in N}\left[\widehat{I}_{n}(t)z_{n}(t)p_{n}(t)-I_{n}(t) z_{n}(t)r_{n}(t)\right].\end{split} \tag{13}\]  
Proof.: See proof of theorem 4.1. We minimize \(\Delta_{V}(t)\)'s upper bound to minimize it. Since \(B\) is a constant, we minimize \(D_{V}(t)\).
# A Survey of Public Key Infrastructure-Based Security for Mobile Communication Systems
## 2 Mobile Communications Security Issues  
### 2G-GSM Security  
There had been breakthroughs related to the 2G specifications as time went on; many new cryptographic algorithms for GSM, enhanced circuit switched data (ECSD), general packet radio service GPRS, and enhanced data rates for GSM evolution (EDGE) that can be implemented on dual-mode phones were designed in 2002, and finally, A5/3 for GSM and ECSD/EDGE, GEA3 for GPRS, and f8 for universal mobile telecommunication system (UMTS) were proposed, all of them having a similar structure. Considering the network access security, designs were proposed for 2G mobile systems such as GSM and CDMA to counter external attacks, but these designs have led to numerous interception attacks [4; 5; 6; 7]. Although the security mechanisms of the GPRS are GSM are similar, GPRS uses the GPRS encryption algorithm (GEA), which currently has three versions: GEA1, GEA2, and GEA3, instead of using the A5 algorithm; In GPRS, the end terminal of encryption is moved towards a deeper point in the network, i.e., the serving GSN (SGSN). It requires the physical layer of the GSM to perform the encryption, and the accomplishment is seen at the logical link control (LLL) layer of the GPRS. Apart from its new offered applications, the UMTS scrutinized the GSM security problems and solved most of them. The main reasons behind GSM security problems were due to the fact that its security was provided by obscurity for the UMTS algorithms to be openly designed; therefore, its algorithms have not faced serious problems. In addition, with the current technology, the theoretical attacks proposed are not practically feasible. Nevertheless, many works proposed to provide security solutions by using PKI techniques.
# An Overview on the Applications of Matrix Theory in Wireless Communications and Signal Processing
## 3 Matrix Applications in Signal Estimation Theory  
Estimation theory is a branch of statistics that aims to estimating the values of parameters based on measured or empirical data. Applications of parameter estimation are prevalent in the area of wireless communications and signal processing. In this section, we briefly discuss how matrix theory can be applied to simplify signal estimation problems, reduce computational complexity, develop adaptive (recursive) filters and solve ill-conditioned estimation problems.
# Electrical Equalization Analysis of PAM-4 Transmission in Short-Reach Optical Systems
## 5 Conclusions  
The main finding from the results is the equalizer's sensitivity to the presence of noise. The equalizer should handle the ISI and the channel response, while considering the noise and making a relative compensation when using the MSE criterion. In practice, as evident from the last two sections, we saw that the noise has a dominant impact on the equalizer performance.  
Both FFE and DFE perform well with the non-linear ISI caused by the direct detection. Increasing the ISI from the TX filter or the CD did not degrade the performances.
# Novel Real-Time Compensation Method for Machine Tool's Ball Screw Thermal Error
## 2 Thermal Equilibrium Analysis
### Components of Screw Thermal Error  
The thermal error of the ball screw feed system caused by the ambient temperature \(TE_{a}\) can be calculated from a linear equation [22]:  
\[TE_{a}=\alpha\times\Delta T_{a}\times X_{N}, \tag{1}\]  
where \(\alpha\) is the expansion coefficient of the screw material, \(\Delta T_{a}\) is the change of ambient temperature, and \(X_{N}\) is the current position of the axis.  
The other part of the screw's thermal error is caused by the feed movement of the ball screw feed system, denoted by \(TE_{m}\). The total thermal error of the screw can be expressed as:  
\[TE_{total}=TE_{a}+TE_{m} \tag{2}\]
## 5 Existing Cybersecurity Solutions for Smart Manufacturing
### Security with Software-Defined Networking (SDN)  
The manufacturing execution system (MES) sits in between the ICS and front-end applications used by human operators. The data collected by the sensors are stored, analyzed, and compared to determine the performance of the machine and the quality of the product coming out of the manufacturing process. The intercommunication between these systems makes the programable logic controllers (PLC) vulnerable to attacks causing hindrance in production and exposing other connected systems such as MES. Attacks, based on network scanning and probing are the most usual ones for MES and a distributed network architecture consisting of multiple segments guarded by firewalls is an effective countermeasure for such kinds of attacks.  
In these types of networking environments, it is necessary to construct secure and flexible access control rules which give rise to the need for software-defined networking (SDN). SDN is an architecture where the forwarding function (data plane) of the networking devices is decoupled from their network control function (control plane) making it centrally manageable programmatically [19]. The SDN architecture allows network administrators to dynamically control the traffic flow by automated SDN programs which are independent of the underlying software of the networking devices. This approach greatly minimizes the threats in ICS networks as they can be accessed and controlled on demand. Figure 5 shows the network structure of the SDN firewall designed for smart manufacturing systems [20]. In this architecture, the SDN firewall consists of two critical components, the packet filtering function which allows or denies access to the ICS based on the access control rules, and the firewall controller stores the configuration of the manufacturing system and the rules created and managed by the administrators.  
Figure 5: SDN Firewall for Smart Manufacturing.
# Error-Bounded Learned Scientific Data Compression with Preservation of Derived Quantities
###### Abstract  
Scientific applications continue to grow and produce extremely large amounts of data, which require efficient compression algorithms for long-term storage. Compression errors in scientific applications can have a deleterious impact on downstream processing. Thus, it is crucial to preserve all the \"known\" Quantities of Interest (QoI) during compression. To address this issue, most existing approaches guarantee the reconstruction error of the original data or primary data (PD), but cannot directly control the problem of preserving the QoI. In this work, we propose a physics-informed compression technique that is composed of two parts: (i) reduction of the PD with bounded errors and (ii) preservation of the QoI. In the first step, we combine tensor decompositions, autoencoders, product quantizers, and error-bounded lossy compressors to bound the reconstruction error at high levels of compression. In the second step, we use constraint satisfaction post-processing followed by quantization to preserve the QoI. To illustrate the challenges of reducing the reconstruction errors of the PD and QoI, we focus on simulation data generated by a large-scale fusion code, XGC, which can produce tens of petabytes in a single day. The results show that our approach can achieve a high compression amount while accurately preserving the QoI within scientifically acceptable bounds.  
Keywords:data compression; autoencoders; error guarantees; moment preservation; constraint satisfaction; quantization; fusion application +
Footnote †: journal:
# Geolocation, Calibration and Surface Resolution of CYGNSS GNSS-R Land Observations
## 1 Introduction
Additionally, of great importance to land remote sensing applications is the achievable surface resolution under different observation conditions. When the land surface is relatively smooth, with respect to the L-band incident wavelength, the received signal at the spacecraft will be coherent. In theory, this will result in a surface reflection footprint of approximately the first Fresnel zone, integrated over the motion of the surface reflection point during instrument processing. In this paper,the actual minimum feature detection resolution for coherent reflections will be investigated using an ancillary data set of river widths. These fundamental differences between ocean and land GNSS-R observations necessitate more detailed consideration of the geolocation, calibration, and spatial resolution of land observations. Following this introduction, Section 2 presents a land geolocation algorithm that uses three criteria for identifying the dominant scattering location on Earth's surface. Section 3 then describes the calibration of GNSS-R land observations, including the calculation of both diffuse and coherent observation data products, including land \(\sigma_{0}\), reflectivity, and coherence estimate. Section 4 presents the results of analysis to estimate the spatial resolution achievable by CYGNSS 1 Hz and 2 Hz coherent land observations, while Section 5 provides general discussion on the algorithms and on-going work and Section 6 provides a summary of the results of the paper.
# Adaptive Solar Power Forecasting based on Machine Learning Methods
## 2 Principle and Framework of the ASPF
### Rules for Network Updating and Feedback  
Figure 4 shows the rule of the _Updating judgment_ function in Figure 3. \(P_{rev}\) is the day-ahead revised predictive PV power obtained from SDM or NDM, which is compared with \(P_{act}\), and \(\Delta\) is the maximum absolute error between \(P_{rev}\) and \(P_{act}\). \(\Delta_{2}\) represents acceptable error threshold between the revised solar power and the actual PV power in the current adaptive system. In addition, \(\Delta_{2}\) is updating as more data collected and processed, and will become smaller and smaller. It is also an indicator to measure the accuracy of predictions.  
In the _Updating judgment_, if \(\Delta\) is larger then \(\Delta_{2}\), a trigger signal \(R_{s}\in[0,1]\) is produced to activate BPNN. \(R_{s}=1\) and BPNN is updated; otherwise, BPNN remains unchanged. Thus, the _Updating judgment_ is mainly applied to renew the BPNN to guarantee accurate predictions. Here, \(\Delta_{2}\) is calculated by function Equations (9) and (17) in Sections 3 and 4. We apply this both for SDM and NDM.  
Through the above three parts, we compose the ASPF, and the complete realizations in every detail are presented next.
## 8 Discussion and Conclusions
In the case of full EV, challenges mainly concern autonomy, battery technology, vehicle weight, industrial interoperability and infrastructure in case of travel mode for long distance transport. * Battery technology is linked to innovations in electrochemical research to achieve high capacity with low mass and volume. * The reduction in the weight of the vehicle apart from the problem of the battery, concerns the new materials of the structure. This implies safety expertise in the field of mechanical and electromagnetic compatibility (EMC). * Apart from transport close to home where the EV usually uses an identified IPT, the problem of interoperability of IPT installations appears to be a critical difficulty. * In the case of a mode of travel for long-distance, the question of infrastructure poses specific technical and economic problems. The first concerns the adapted technologies taking into account uncontrolled weather conditions, the optimization of the primary ground side of the IPT, the intelligent communication between the EV and the infrastructure system, the organization of flows and the management of interoperability. The economic problem mainly concerns the size of the demand due to the obvious viability of the installation. In the case of the RHEV, the problems of autonomy, battery and weight are relatively reduced. The issues of interoperability and infrastructure for the mode of travel can be circumvented. However, the issue in this case concerns the dual use of dexterous and clean engines with high efficiency with minimal pollution. Different investigative challenges emerge from the last analysis. These mainly concern battery technology, new compatible structural materials, interoperability of IPT installations, infrastructure technology and optimization of IPT for the mode of travel. This research received no external funding. Not applicable. The data presented in this study are available on request from the corresponding author. The author declares no conflict of interest.
## 4 Experiment and Analysis
### Semantic3D  
Table 2 reproduces the experimental results of several classic algorithms for semantic segmentation of 3D point clouds in Semantic3D dataset, including SnapNet_[17], RF_MSSF [30], SEGCloud [31], ShellNet [28], GAENet [32], SPG [33], KPConv [34], and RandLA-Net [5]. In the projection-based approach, SnapNet_ projects 3D point clouds into multi-view images and then extracts features using traditional 2D convolutional neural networks. The projection process results in the loss of point cloud geometry information. Therefore, the accuracy of semantic segmentation of the 3D point cloud is very poor. SEGCloud divides the point cloud into a set of occupancy voxels. Then, 3D convolutional neural networks is applied to segment the point cloud scene. In this case, the voxel size is a very important hyperparameter, and the quantization error of voxelization will lead to the loss of geometric information. The high precision of voxel size will bring the burden of calculation. Point-based methods include ShellNet, GACNet, SPG, KPConv, and RandLA-Net, described in related work sections. Compared with other methods, RandLA-Net has better segmentation accuracy, and it is used as a representative to compare with our method. The results showed that LDE-Net was superior to all of them in terms of the mIoU and the OA.  
Figure 5 shows the semantic segmentation of LDE-Net and RandLA-Net in detail. The edges obtained by RandLA-Net are very rough and can cause some trouble. In labeling each point, LDE-Net adds a local feature aggregation classifier that takes into account the semantic information of the surrounding points. As a result, the edges obtained by LDE-Net are very smooth and precise. As shown in Figure 5, the red boxes are all details of how our method (LDE-Net) compares to RandLA-Net. Table 2 shows the accuracy of LDE-Net on the semantic3D dataset. Due to the addition of the global feature extraction modules and two local feature extraction modules, LDE-Net is 0.2% higher than RandLA-Net on the mIoU.  
Figure 5: Semantic segmentation of colored 3D point clouds in semantic3D.
# Dependence of the Preload on the Tightening Torque for Hydraulic Plugs
###### Abstract  
In hydraulics, threaded plugs are used to close various manufacturing holes and other fluid channels. They are preloaded to ensure sufficient sealing force. Since the range of recommended thread and underhead friction coefficients for preloaded threaded connections in the literature is very wide, they are not suitable for accurate determination of the preload-torque relationships of plug-valve connections. In the study, two non-standard plugs with metric threads were equipped with strain gauges and repeatedly tightened three times in valve housings under lubricated and unlubricated conditions. The preload and tightening torque were measured. (1) Although the plug-valve connections had a similar geometry with the same surface roughness of the contacting surfaces, the average overall friction coefficient (uniform thread and underhead friction coefficient) and torque coefficient differed between the two connections in the unlubricated and lubricated conditions by 16% and 18%, respectively. This indicates that even small geometrical differences can have a considerable influence on these coefficients. The overall friction and torque coefficients were between 8% and 17% higher in the unlubricated condition than in the lubricated condition (not statistically proven). (2) The overall friction and torque coefficients decreased with repeated tightening under lubricated conditions. This influence decreased with the number of tightening repetitions. (3) Consideration of the minimum and maximum thread and underhead friction coefficients given in VDI 2230 would lead to an error in the estimated preload of \(-15\%\) to \(+86\%\). In conclusion, for accurate determination of the preload-torque relationship of the plug-valve connections, measurements considering repeated tightening are crucial. These should be performed for each type and size of plug-valve connection separately. To minimize the repeated tightening influence, it is recommended to re-tighten the connections several times before leaving production.  
preloaded connections; hydraulic plugs; tightening test; clamping force; strain gauges; pretensioned fasteners; nut factor; torque coefficient; overall friction coefficient; repeated tightening Article