esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Proposed Method
### Optimization  
The objective function in Equation (26) is a continuous function with respect to the latent variables and kernel parameters and can be generally maximized by gradient-based optimization, such as conjugate gradient descent [51] and the quasi-Newton method [52]. Generally, gradients are derived from a chain role of differentiation, and, thus, we need to compute the differentiation of the SE kernel with respect to the latent variables. This differentiation is given as follows:  
\[\frac{\partial}{\partial x_{q}}k(\mathbf{x},\mathbf{x}^{\prime})= \sum_{p=1}^{N_{\text{reg}}}w_{p}\biggl{[} -4\pi^{2}(x_{q}-x_{q}^{\prime})v_{q}^{(p)}-2\pi\mu_{q}^{(p)}\tan \Bigl{\{}2\pi(x_{q}-x_{q}^{\prime})\mu_{q}^{(p)}\Bigr{\}}\Bigr{]}\\ \times\prod_{q=1}^{Q}\exp\Bigl{\{}-2\pi^{2}(x_{q}-x_{q}^{\prime}) ^{2}v_{q}^{(p)}\Bigr{\}}\cos\Bigl{\{}2\pi(x_{q}-x_{q}^{\prime})\mu_{q}^{(p)} \Bigr{\}}\biggr{]}. \tag{27}\]  
Furthermore, the differentiations with respect to the kernel parameters \(w_{p}\), \(v_{q}^{(p)}\), and \(\mu_{q}^{(p)}\) are given as follows:  
\[\frac{\partial}{\partial w_{p}}k_{SM}(\mathbf{x},\mathbf{x}^{ \prime}) =\prod_{q=1}^{Q}\exp\Big{\{}-2\pi^{2}(x_{q}-x_{q}^{\prime})^{2}v_{ q}^{(p)}\Big{\}}\cos\Big{\{}2\pi(x_{q}-x_{q}^{\prime})\mu_{q}^{(p)}\Big{\}}, \tag{28}\] \[\frac{\partial}{\partial v_{q}^{(p)}}k_{SM}(\mathbf{x},\mathbf{x }^{\prime}) =-2\pi^{2}(x_{q}-x_{q}^{\prime})^{2}\exp\Big{\{}-2\pi^{2}(x_{q}- x_{q}^{\prime})^{2}v_{q}^{(p)}\Big{\}}\cos\Big{\{}2\pi(x_{q}-x_{q}^{\prime})\mu_{q}^{(p)} \Big{\}}, \tag{29}\]\[\frac{\partial}{\partial\mu_{q}^{(p)}}k_{SM}(\mathbf{x},\mathbf{x}^{ \prime})=-2\pi(x_{q}-x_{q}^{\prime})\tan\Big{\{}2\pi(x_{q}-x_{q}^{\prime})\mu_{q }^{(p)}\Big{\}}\\ \times\exp\Big{\{}-2\pi^{2}(x_{q}-x_{q}^{\prime})^{2}v_{q}^{(p)} \Big{\}}\cos\Big{\{}2\pi(x_{q}-x_{q}^{\prime})\mu_{q}^{(p)}\Big{\}}. \tag{30}\]  
We optimize the parameters with Equations (27)-(30) as well as \(\frac{\partial\mathcal{L}}{\partial\mathbf{K}_{\text{MN}}}\) and \(\frac{\partial\mathcal{L}}{\partial\mathbf{K}_{\text{MM}}}\), which have been derived in previous research [48]. After optimization, we obtain the optimal latent variables \(\mathbf{X}\), spectral \(\mu_{p}^{(q)}\), weights \(w_{p}\), and lengthscales \(v_{q}^{(p)}\). As a result, we can reduce the number of hyperparameters and improve the tractability of our model.
# A Comparison of Blockchain Recovery Time in Static and Mobile IoT-Blockchain Networks
## 6 Conclusions  
This study investigates the recovery time of IoT-blockchain systems with infrastructure-less underlying networks that can avoid SPoF. We first create emulated and real IoT-blockchain networks with the private Ethereum blockchain and three ad hoc routing protocols, OLSR, BATMAN, and BABEL. We then extensively evaluate the recovery time of the systems in static and mobile scenarios. The experimental results show that BATMAN has the best ability to recover the blockchain communication in a real IoT-blockchain system. More specifically, it recovers 74.9% faster than OLSR and 59.8% faster than BABEL in the static scenario and 73.9% faster than OLSR and 61.8% faster than BABEL in the mobile one. In the emulated IoT-blockchain system with more nodes, BATMAN still maintains its outstanding features. In the static scenario, BATMAN achieves 69% and 60% faster recovery than OLSR and BABEL, respectively. BABEL, in turn, restores faster than OLSR, which has the second-lowest recovery time. In a bigger network with node mobility, BATMAN outperforms the others and can recover the blockchain in 6.22 s, 5.92 s, and 5.95 s, following the different stages, respectively  
Conceptualization, Y.S. and K.N.; methodology, Y.S. and K.N.; software, Y.S.; validation, Y.S. and K.N.; formal analysis, Y.S.; investigation, Y.S. and K.N.; resources, Y.S.; writing--original draft preparation, Y.S. and K.N.; writing--review and editing, K.N. and H.S.; supervision, K.N. and H.S.; project administration, K.N.; funding acquisition, K.N. All authors have read and agreed to the published version of the manuscript.  
This work was supported in part by the Japan Society for the Promotion of Science (JSPS) under Grant 20H0417; and in part by the Japan Science and Technology Agency (JST) through the establishment of university fellowships towards the creation of science technology innovation, Grant Number JPMJFS2107.  
**Data Availability Statement:** Not applicable, the study does not report any data.  
**Conflicts of Interest:** The authors declare no conflict of interest.  
Figure 10: Recovery time of emulated IoT-blockchain in mobile scenario. (**a**) Recovery time; (**b**) Average recovery time with components.
## 1 Introduction
In terms of Bowden cable force transmission, [PERSON] [11] investigated the recoil, hysteresis, and nonlinearity caused by cable friction and proposed a method that solely relies on force and position sensors at the drive end to estimate actuator parameters at the output end. Validation results demonstrated an approximate maximum full-scale error of only 7% for this method. [PERSON] [12; 13] addressed issues related to force transmission modeling in flexible cable systems, precise position control, and elongation studies. Furthermore, they conducted work on modeling tactile feedback properties and introduced a bidirectional actuation system using two independent cables. They applied a model-based feedforward compensation method for open-loop control, eliminating the need for any feedback information from the remote end, allowing the system to adapt to variations in transmission path configurations for precise position control. [PERSON] [14] proposed a tendon sheath-driven system for robotic hands, analyzed its static force transmission characteristics, established a Dahl dynamic friction model, and introduced a force control algorithm incorporating feedforward friction compensation based on the static friction model. [PERSON] [15] examined the tactile feedback of a flexible actuator, assessed the force and displacement transmission of a Bowden cable system at various bending angles, and applied error compensation using disturbance observer technology. [PERSON] [16; 17; 18; 19] conducted extensive research on the nonlinear aspects of friction in Bowden cable transmission. They introduced a Coulomb friction model based on static force balance for studying Bowden cable transmission characteristics. The LuGre dynamic friction model was employed to describe the motion of a single Bowden cable transmission system, and a control algorithm combining sliding mode and friction compensation was proposed based on the Coulomb friction model. [PERSON] [20] developed a Bowden cable friction model using micro-element analysis and performed experiments to investigate how velocity, radius of curvature, friction factor, and total curvature affect the force transmission characteristics of Bowden cables. Additionally, they suggested new friction compensation and displacement compensation algorithms. Although the above research has set up the force transfer of the Bowden cable, established the friction model, and analyzed the friction between the wire rope and the wire sheath, the main research is purely Bowden cable drive or applied to robot control. Flexible exoskeletons usually have a piece of fabric behind the Bowden cable, and the fabric will eventually deliver force to the human limb. The addition of fabric not only brings a certain force loss but also causes the position to be unable to be accurately controlled due to its elastic deformation. Therefore, the precise force/displacement control of the Bowden cable-fabric is a key technology for the practical application of flexible exoskeletons. This paper focuses on the precise control of the force/displacement of the flexible exoskeleton Bowden cable-fabric.
# Temporal Action Detection in Untrimmed Videos from Fine to Coarse Granularity
## 4 Experiments
### Comparison with State-of-the-Art Methods
For example, the node \"Playing sports\" on level-4 has child nodes \"Archery\", \"Baton twirling\", etc. on level-5. From the hierarchical action categories definition, a subset can be formed by including all action categories that belong to a certain node. Due to the large scale, it is difficult to evaluate action detection methods on this datasets. Same with the study [18], we also conduct the experiment on a subset of ActivityNet1.3. We select the \"Playing sports\" node on level-4 from the categories hierarchy, which has 26 action categories. Table 5 show AP for each category and mAP of the subtset 'Playing sports'. We present the performance of the state-of-the-art method on ActivityNet in Table 6. Besides recognition result, foe each method, we also specify the version of ActivityNet, and the involved subsets. Although, the compared methods are evaluated on different ActivityNet versions, and some of them and our method are evaluated on a subset of ActivityNet, the results show that our method gains a comparable performance to the ActivityNet dataset. \begin{table}
\begin{tabular}{l l l} \hline ActivityNet [41] & 9.7 & ActivityNet1.3 \\ R-C3D [25] & 28.4 & ActivityNet1.3 \\ Generalization [51] & 40.7 & ActivityNet1.3 \\ TURN [18] & 37.1 & ’Participating in Sports, Exercise, or Recreat.’ subset of ActivityNet1.1 \\ TURN [18] & 41.2 & ’Housework’ subset of ActivityNet1.1 \\
**Out method** & 37.9 & ’Playing sports’ subset of ActivityNet1.3 \\ \hline \end{tabular}
\end{table} 
Table 6: Comparison with state-of-the-art methods on ActivityNet: mAP (%) with 0.5 tIoU. \begin{table}
\begin{tabular}{l l|l l|l l} \hline \hline Archery & 31.2 & Doing a powerbomb & 33.5 & Playing kickball & 47.2 \\ Baton twirling & 23.6 & Doing motocross & 51.6 & Pole vault & 38.2 \\ Bungee jumping & 46.8 & Hammer throw & 32.7 & Powerbocking & 29.5 \\ Camel ride & 48.2 & High jump & 33.4 & Rollerblading & 23.8 \\ Cricket & 37.9 & Hurling & 35.2 & Shot put & 32.1 \\ Croquet & 43.1 & Javeelin throw & 34.8 & Skateboarding & 41.2 \\ Curling & 29.3 & Long jump & 56.7 & Starting a campfire & 39.2 \\ Discus throw & 44.1 & Longboarding & 44.3 & Triple jump & 27.9 \\ Dodgeball & 36.9 & Paintball & 43.1 & **mAP** & **37.9** \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Detection AP for each category and mAP of the subset ’Playing sport’ of ActivityNet1.3 with 0.5 tIoU. Figure 9: Detection Average Precision (AP) for each action categories with 0.5 tIoU on THUMOS’14.
# An Islanding Signal-Based Smooth Transition Control in AC/DC Hybrid Micro-Grids
## 4 Simulation and Discussion
### Comparision of Different Transition Controls
\begin{table}
\begin{tabular}{c c c c c} \hline
**Transition Control** & \(\Delta\mathbf{U_{demax}}\) & \(\Delta\mathbf{U_{acmax}}\) & **Transition Time** & **Succeed? (Y/N)** \\ \hline Direct transition & 11.31\% & 6.7\% & 0.46 s & Y \\ Traditional smooth transition & 7.2\% & 10\% & 0.31 s & Y \\ Proposed smooth transition & 2.98\% & 3.77\% & 0.25 s & Y \\ \hline \end{tabular}
\end{table} 
Table 1: Hybrid micro-grid operating status, from grid-connected mode to islanding mode. \begin{table}
\begin{tabular}{c c c c c} \hline
**Transition Control** & \(\Delta\mathbf{U_{demax}}\) & \(\Delta\mathbf{U_{acmax}}\) & **Transition Time** & **Succeed? (Y/N)** \\ \hline Direct transition & 26.15\% & 2.31\% & - & N \\ Traditional smooth transition & 9.53\% & 2.34\% & 0.23 s & Y \\ Proposed smooth transition & 9.54\% & 2.33\% & 0.23 s & Y \\ \hline \end{tabular}
\end{table} 
Table 2: Hybrid micro-grid operating status from islanding mode to grid-connected mode. Figure 12: DC voltage, AC voltage, and frequency with islanding signal-based smooth transition control. Figure 13: Regulation state of the direct transition control. a successful state following. Therefore, the DC voltage offset in the traditional transition control is smaller than that in the direct transition control. However, due to the fact that the control parameters of the traditional transition controller are set for islanding operation, the regulators of the AC battery and ILC \(d\)-axis will become saturated, and then cannot realize state following successfully. The regulation state step changes even more than with direct transition control during the switching time. Therefore, in traditional smooth transition control, the AC voltage offset is the largest. During \(t=\) 1.5-3 s, the micro-grid operates in islanding mode. When the operating mode is switched at \(t=\) 3 s, the direct transition fails, because of the large step change in the regulator. The traditional smooth transition control achieves a good state following and realizes smooth transition, as shown in Figure 14.  
According to a comparison of the different approaches, it can be seen that the proposed smooth transition control can ensure non-step change of the regulation state at any transition moment to implement a smooth transition between grid-connected mode and islanding mode, as shown in Figure 15. As a result, the transient impacts during transition time is minimized regardless of the power exchange. The fluctuation is small, and the time to reach a new stable state is short.
## 3 System Design and Implementation
### Implementation
#### 3.3.4 USB Adapter Dongle  
Several adapters have been tested with Raspberry Pi, but the ad hoc support was not available. Therefore, we selected the DWA-131 (D-link Wireless N Nano USB Adapter) as shown in Figure 3D, which is a convenient wireless connectivity solution for microcomputers, PDAs (Personal Digital Assistants), and desktop or notebook PCs. It comes with the stable driver for Raspberry Pi, which needs to be installed prior to use.
## 2 Experimental Measurements  
Experimental measurement trials carried out at the Port of Lubeck, Germany, serve as ground truth. They are used to construct the training and test sets for the evaluation and the informative path-planning simulation.  
The 5G measurement unit consists of an LPPM[X]-36-55-[VAR] 4x4 MIMO antenna manufactured by Panorama Antennas Ltd. (London, UK) and an RM500Q 5G modem manufactured by Quectel (Shanghai, China). The modem is attached to a Raspberry Pi Compute Module 4 (CM4) on a Dual Gigabit Ethernet 5G/4G Base Board manufactured by Waveshare (Shenzhen, China). The significance of the combined baseboard lies in the fact that a USB connection between the single-board computer and the modem may result in significant interference with the global navigation satellite system (GNSS) of the UAV (cf., [15]).  
For a safe and stable flight, the UAV has to have a sufficient payload to carry the measurement unit with a good margin. The Matrice 300 (M300) quadcopter by DJI (Shenzhen, China) was chosen as a platform due to its 2.7 kg payload capacity. It also offers an abundance of obstacle avoidance sensors and an onboard software development kit (OSDK) to control the UAV and collect telemetry data. Onboard communication with the M300 UAV platform is achieved using a Manifold 2-C single-board computer by DJI (Shenzhen, China) mounted on top of the UAV. The measurement unit is mounted below the UAV body and connected to the computer via Ethernet cable, again avoiding a USB connection. The mounted measurement setup is shown in Figure 1.  
Data acquisition and synchronization are achieved using the Robotic Operating System (ROS) publisher and subscriber framework. The CM4 queries signal parameters via AT commands using a serial connection to the modem. The data are then published in an ROS topic. Similarly, the manifold uses the OSDK to publish GNSS information on an ROS topic. A data logger subscribes to both topics using a rate of 1 Hz for the signal strength publisher.  
Figure 1: 5G mMeasurement unit mounted below a DJI Matrice 300 UAV.  
Figure 2: Flight planning for the measurement flight in UgCS ground control software by SPH Engineering (Riga, Latvia). The green lines depict the flight routes, and the red transparent areas mark no-fly zones around higher structures like light poles.  
The resulting measurements of the received reference signal strength (RSRP) are shown in Figure 3. The simulated antenna pattern is also plotted to highlight how the measurements coincide with the direction of the antenna's main lobe.  
The measurements for the cuboid on the left side of Figure 3 were performed twice with the same flight route. The root-mean-square error of the one trial was compared to the nearest neighbor interpolation of the other trial. The error between the interpolation of the first trial and that of the second trial was 10.23 dBm.
## 5 Case Study: Smart Conservation of Cultural Heritage
### Evaluation and Results  
The proposed CA middleware was deployed for a period of 8 weeks. Initially contextual data were collected by a relatively small number of installed sensors, but tests were also performed on scalability and performance with the artificial data of thousands of sensors. The behavior of the proposed middleware was evaluated with respect to the following criteria: (a) representation, (b) reasoning veracity and (c) computational performance and scalability.
## 5 Discussion
### Pore Evolution Models of Different Lithofacies
#### 5.3.1 Siliceous Shale  
Siliceous shales with high quartz and TOC contents were usually developed in the WF Formation and the lower part of the LMX Formation. Previous studies showed that siliceous shale lithofacies were generally deposited in anoxic water columns with high paleoproductivity, resulting in relatively high TOC content [63; 28; 64]; moreover, a large number of siliceous organisms were preserved in the siliceous shales (Figure 15a).  
In the early diagenetic stage, through microbial action, pyrite, calcite, and dolomite were gradually formed (Figure 15b). Combined with the Fe\({}^{3+}\) supplied by clay reactions, the Fe-rich dolomite was developed around the dolomite. The primary pores were destroyed under the mechanical compaction, resulting in decreased porosity. Siliceous shales contain abundant microcrystalline; therefore, the large number of authigenic quartz formed a rigid framework and an effective stress support system, which protected the intergranular pores from collapsing and weakened the further destruction of the reservoir pore system caused by compaction (Figure 15b). Siliceous shale generally exhibited relatively weak compaction. Therefore, the preservation degree of primary pores in siliceous shales is relatively higher than that of argillaceous shales and siliceous-argillaceous mixed shales. In the middle diagenetic stage (Figure 15c), the organic matter entered into the oil window, and a large amount of liquid hydrocarbon was generated and filled into intergranular pores. At this stage, carbonate and feldspar may be partially dissolved with the organic acids that generated from organic matter maturation, forming intraparticle pores. With increased temperature, smectite was gradually transformed into illite and smectite-illite mixed layers. In the late diagenetic stage (Figure 15d), the organic matter entered into the dry gas window, and abundant organic pores were produced with further thermal cracking of oil and solid bitumen into pyrobitumen. Organic pore is the dominant pore type in siliceous shale that is characterized by high authigenic quartz content.
# Effect of Holes on Dynamic Crack Propagation under Impact Loading
###### Abstract  
In civil, geotechnical, and mining engineering, the investigation of the holes' effect on dynamic crack propagation is essential because it can be used to predict possible fracture and protect cracked structures being further damaged. In this paper, a specimen made from polymethyl methacrylate (PMMA) with a pre-crack and two holes was proposed, and the Split-Hopkinson pressure bar was employed to investigate the effect of holes on dynamic crack propagation under impact loading. Notably, the locations of the holes were well designed with different two-hole spacing (12 mm, 16 mm, and 20 mm) and crack-hole distance (15 mm, 30 mm, and 45 mm). Crack propagation gauges were applied to monitor the fracturing time and crack extending velocity. The interaction characteristic between the crack and two holes was studied numerically using the AUTODYN code. In the numerical models, the failure criteria of maximum tensile stress and softening damage were employed for brittle material. The crack path, the propagating velocity, the particle velocity vector, and the stress state between the holes were analyzed. The calculation results indicate that compressive stresses between the two holes induced by the deformation of the holes play a crucial role in confining the vertical crack propagation. Both experimental and numerical results demonstrate that the holes have a suppressing action on the moving crack; as the two-hole spacing decreases, the suppressing action intensifies.  
impact loading; crack propagation; crack arrest; PMMA; numerical simulation +
Footnote †: journal: Applied Physics Letters B
## 4 A Novel Bagging Method Based on Ensemble Margin
### Ensemble Margin Based Data Ordering  
The informative instances such as class decision boundary samples and difficult class instances play an important role in classification particularly when it is imbalanced classification. These instances generally have low ensemble margins. To utilize the relationship between the importance of instances and their margins effectively in imbalance learning, we designed our class imbalance sampling algorithm based on margin ordering.  
Let us consider a training set denoted as \(S=\left\{X,Y\right\}=\left\{x_{i},y_{i}\right\}_{i=1}^{n}\), where \(x_{i}\) is a vector with feature values and \(y_{i}\) is the value of the class label. The importance of a training instance \(x_{i}\) could be assessed by an importance evaluation function which relies on an ensemble margin's definition and is defined by Equation (5). _The lower the margin value (in absolute value), the more informative the instance \(x_{i}\) is and the more important it is to consider for our imbalance sampling scheme._  
\[W(x_{i})=1-|margin(x_{i})| \tag{5}\]  
To solve the problem previously mentioned related to the margins (both supervised and unsupervised) based on a sum operation, a shift is performed before data importance calculation. The shifted margin values are achieved by subtracting the minimum margin value of the samples of the training set which are correctly classified from their original margin values. An example is used to explain the margin shift procedure in Figure 2.