esa-sceva/satcom-chunk-collection huggingface dataset:
# Analysis of a Supercapacitor/Battery Hybrid Power System for a Bulk Carrier
## 2 System Description  
### Target Ship  
In this paper, a medium-sized bulk carrier was selected as the target ship. The target ship's deadweight was about 50,000 tons, and it had five hatches. The target ship was fitted with three gensets as power sources and four electric-driven deck cranes (Figure 1). In addition to these deck cranes, windlass/mooring winches were also of the electric-driven type controlled by each motor drive. Although hydraulic-driven equipment has been used for a long time, it has many disadvantages including low efficiency, high noise and vibration, high maintenance cost, pollution risk through oil, etc. [21]. Therefore, the use of electric-driven equipment has been increasing in the marine industry.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Type** & **Energy Density** & **Power Density** & **Life Cycles** & **Voltage** & **Charging/Discharging** \\  & **(Wh/kg)** & **(W/kg)** & **(cycles)** & **(V/cell)** & **Time** \\ \hline Lithium-ion Battery (LIB) & 150–250 & 50–2,000 & 500–2,000 & 3.6–4.2 & Minutes – Hours \\ Supercapacitor (SC) & 5–10 & –100,000 & 500,000–2,500,000 & 2.7–3.0 & Seconds – Minutes \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison between lithium-ion batteries (LIBs) and supercapacitors (SCs) [15; 16; 17; 18; 19; 20].  
In general, this kind of bulk carrier has four (4) operation modes, as below:  
* Normal seagoing mode (at sea);
* Port in/out mode (near a port);
* Cargo loading/unloading mode (at a port);
* Harbor mode (at a port).  
First, in the normal seagoing mode, the heaviest electric load is the main engine (M/E) auxiliaries and engine room auxiliaries to propel the ship, and additional electric power is required to maintain the living environment for crews at sea. In the port in/out mode, the heaviest load is the windlass/mooring winches, which are used for lowering/pulling an anchor or hauling-in/winding mooring ropes. The second heaviest load is the ballast pumps, which are used for pumping water into/out from ballast tanks in preparation for cargo loading/unloading or cargo hold cleaning. Additionally, the load on the main air compressors and M/E auxiliary blowers also increases because of the slower speed or frequent stops of a ship while approaching/departing a port.  
In the cargo loading/unloading mode, the heaviest load is the onboard deck cranes used for cargo loading or unloading to the shore-side, which is a highly repetitive process. The second heaviest load is the ballast pumps, which ensures the stability of a ship even though its weight is changed during (un)loading cargo. Lastly, in the harbor mode, the majority of the load comes from the activities of crews such as from the air conditioner compressor, lighting, galley, and laundry equipment, etc.
## 4 Discussion
### Uncertainties and Limitations  
Compared with previous studies, the present study explored the impact of drought on WUE from a seasonal perspective and further authenticated the function of ecosystem WUE in dealing with external environmental interference. Our results confirmed findings of previous studies, such as that WUE responds differently to drought in different seasons and that different vegetation types and ecosystems exhibit different responses to drought. However, there were also some differences between the findings of previous studies and those of the research described in this paper. This may be due to discrepancies in data collection, analysis methods, research cycle, and research area [8]. It should be noted that although previous studies pointed out that the response of ecosystem WUE to drought is affected by many factors, such as vegetation characteristics [24], drought magnitude, and regional characteristics [9; 49; 50], few studies have been conducted on the impact of seasonal drought magnitude on different ecosystem WUE values; therefore, more theoretical and model simulations are needed to verify this conclusion. Furthermore, the SPEI value used to characterize drought in the current study was calculated based on grid data of relatively low-level resolution, so the accuracy was not high, and the time scale of GPP and ET collection data (2001-2019) was relatively short. In addition, the sensitivity of GPP and ET to drought was not taken into account in the current study of the effect of seasonal drought on WUE, which may have an impact on the results of the study. On the other hand, our study did not consider the persistence of the water deficit (i.e., the drought time-scale) and lag effects of drought. It is very important to understand the relationship between WUE and drought, and experimental observational data and high-precision remote sensing data should be used to improve analysis accuracy.
## 4 Secure Federated Statistical Tests
### \(\chi^{2}\)-Test  
For the secure federated \(\chi^{2}\)-test, we follow a similar problem setup as the one presented by [PERSON] et al. [14] where the global dataset is split horizontally amongst clients who collaborate to perform the \(\chi^{2}\)-test to calculate the correlation between two attributes in the dataset, which can be seen from Figure 3.  
Formally, there are \(n\) clients, and client \(i\) has a horizontal partition of the global dataset \(\mathcal{D}_{i}=\{(x,y,v_{xy}^{(i)})\}\) where \(x\in\mathcal{X}\) and \(y\in\mathcal{Y}\) are categories of the contingency table and \(v_{xy}^{(i)}\in\mathbb{Z}_{\geq 0}\) is the observed number of samples in category \(x\) and \(y\) in the client's local contingency table. The global dataset is \(\mathcal{D}=\{(x,y,v_{xy}):v_{xy}=\sum_{i\in[n]}v_{xy}^{(i)}\}\). For the  
Figure 3: Federated setting for \(\chi^{2}\)-test. All the institutions own the same type of data, but for different sample IDs. They jointly do the federated analytics on all the samples without revealing any private data information to each other.  
purposes of the \(\chi^{2}\)-test, the data in the contingency table is assumed to be discrete and marginal statistics are represented as \(v_{x}=\sum_{y\in\mathcal{Y}}v_{xy}\), \(v_{y}=\sum_{x\in\mathcal{X}}v_{xy}\) and \(v=\sum_{x\in\mathcal{X},y\in\mathcal{Y}}v_{xy}\). Lastly, the expectation of \(v_{xy}\) if \(x\) and \(y\) are not correlated is defined a \(\bar{v}_{xy}=\frac{v_{x}v_{y}}{v}\). The task is to then calculate the statistic \(s_{\chi^{2}}(\mathcal{D})=\sum_{x\in\mathcal{X},y\in\mathcal{Y}}\frac{(v_{xy} -\bar{v}_{xy})^{2}}{\bar{v}_{xy}}\) securely from the local contingency tables \(\mathcal{D}_{i}\).  
One key difference between the formulation of [PERSON] et al. and our formulation is that they assume that the marginals \(\{v_{x}\}_{x\in\mathcal{X}}\) and \(\{v_{y}\}_{y\in\mathcal{Y}}\) are non-private information once they have been securely aggregated and therefore can be made public. However, as much of the literature in the area of differential privacy recognizes marginals (which are basically histograms) as private information [23], we make no such assumption and arrive at a solution that is truly end to end secure.  
In our method, we leverage the SIMD packing of MHE schemes to pack the entire contingency table into a single ciphertext and utilize the secure batch division protocol presented in Section 3 to calculate all of the \(\frac{(v_{xy}-\bar{v}_{xy})^{2}}{\bar{v}_{xy}}\) terms simultaneously. Finally, we calculate the inner sum of the resulting ciphertext to calculate the \(s_{\chi^{2}}(\mathcal{D})\) statistic securely.  
It is to be noted that in this case, we can clearly see that \(\bar{v}_{xy}\) must be in the range \([0,N]\) where \(N\) is the total number of samples, which is crucial in obtaining the initial approximation in our secure batch division protocol.
# A Method of Estimating an Object's Parameters Based on Simplication with Momentum for a Manipulator
## 4 Parameter Estimation Simulation  
### Simulation Setup  
The simulations for parameter estimation were conducted using RecurDyn (V8R5), a commercial software package, with the robot model being the UR16e by Universal Robots. As shown in Figure 2, the robot estimates the object parameters by securely grasping and shaking one side of it while its second, third, and fourth joints are positioned at orthogonal angles, forming an elbow-up configuration. A force torque sensor located at the robot's end-effector measures the force and torque at \([R]\). Standard deviations of zero-mean Gaussian distributions were utilized to generate noise for the simulation experiments, as detailed at Table 1, selected based on [24]. Three objects with distinct parameters were employed in the simulations, with the actual parameters of each object provided in Table 2. The objects were chosen to have parameters within the maximum allowed payload of the UR16e [34]. The simulation time step is set to \(1\) ms. The trajectory utilized for the estimation with the proposed method involves guiding the robot's end-effector to move linearly in all directions and then rotate the end-effector separately in the \(x\)-, \(y\)-, and \(z\)-directions. As outlined in Section 3, the robot initially estimates the mass of the object while holding it stationary. Subsequently, linear motion is generated in all directions during the estimation of \(\mathbf{r}_{G/R,b}\), and rotational motion in the \(x\)-, \(y\)-, and \(z\)-directions is carried out separately during the estimation of \(\mathbf{I}\) with a designated motion range. The term'motion range' refers to the extent of linear and rotational motion generated by the robot, expressed in meters and radians, respectively. For instance, if the robot generates a motion with a motion range of 0.1, it generates the motion that moves the grasped object linearly by 0.1 m in all directions and then rotates the object by 0.1 radians in each of the \(x\)-, \(y\)-, and \(z\)-directions separately. In this study, the robot executes estimation motion with motion range of 0.1, 0.05, and 0.025. The trajectory for this motion entails the robot's end-effector moving linearly and angularly to a certain motion range and then rotates the object by 0.1. \begin{table}
\begin{tabular}{c c c c} \hline \hline \multicolumn{1}{c}{**Angular Velocity (mrad/s)**} & \multicolumn{1}{c}{**Force (mN)**} & \multicolumn{1}{c}{**Torque (mNm)**} \\ \hline Low Noise & 0.25 & 50 & 2.50 \\ \hline Moderate (Mode) & \multirow{2}{*}{0.50} & \multirow{2}{*}{100} & \multirow{2}{*}{5} \\ Noise & & & \\ \hline High Noise & 1.00 & 330 & 6.70 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Standard deviations of Gaussian noise used in the simulation experiments.
## 3 Solved Example
### Analysis  
After collecting the data for all runs, their analysis followed. It must be noted here that in order to simplify the example, we consider that there are not significant sample-to-sample differences. First, the contrasts, SS, effects, and parameters for Mo leaching were calculated, using Equations (1)-(4). The calculated values are summarized in Table 3. Then, all main effects and interactions had to be assigned to WP or SP, according to the rules discussed in the Theory section. The assignment is shown in Table 3 as well. The construction of the two normal probability plots of the effects (one for WP and one for SP), in order to detect the significant main effects and interactions, could now follow. These plots are presented in Figure 2a,b. The straight line of the SP effects was automatically created by MATLAB, version 9.7. 0.1190202 (R2019b) [39]; this software was used for the construction of all normal probability plots of this paper. The straight line of the WP effects was drawn manually, since the suggested one did not seem to fit well. The significant effects seemed to be the main effects A, B, C, D, and E, since they fell away from the respective straight lines. The four-factor interaction ABDE could be significant as well; however, interactions higher than second order are usually non-significant [1]. Although \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**WP (or Run Order)** & **Run Name** & **A** & **B** & **C** & **D** & **E** & \(\mathbf{y_{Mo}}\) & \(\mathbf{logy_{Mo}}\) \\ \hline  
bcde & \(+\) & \(+\) & \(+\) & \(+\) & \(+\) & 63.09 & 1.800 \\ \hline \multicolumn{2}{c}{Average} & & & & & & 13.99 & 0.827 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Design matrix of the split-plot experiment and response variables (mg/cell). this interaction was decided in the end to be included in the model, its addition should be treated with caution, i.e., based on the later analysis (e.g., plots of residuals), it should be decided if it makes sense. After selecting the significant main effects and interactions, the proposed model was constructed:  
\[\widetilde{\text{Log}\big{(}y_{Mo}\big{)}}=0.827+0.152\cdot\text{x1}+0.185\cdot \text{x2}+0.364\cdot\text{x3}-0.111\cdot\text{x4}+0.203\cdot\text{x5}-0.084 \cdot\text{x1}\cdot\text{x2}\cdot\text{x4}\cdot\text{x5} \tag{15}\]  
The next important step in the analysis must always be to check the residuals. For that purpose, the fitted values should first be calculated. This calculation was done in the usual way, i.e., each fitted value was found by substituting the respective coded values into the proposed model of Equation (15). The ordinary residuals were also calculated as usual, by subtracting each fitted value from the respective response. After that, the two types of residuals relating to split-plots had to be calculated. First, the WP residuals were computed. As described in the Theory section, a WP residual is the average of all the ordinary residuals e of the runs belonging to the particular WP. Thus, all runs belonging to the same WP have the same WP residual. For example, Table 4 shows
# Jam Mitigation for Autonomous Convoys via Behavior-Based Robotics
## 3 Materials and Methods
### Behavior Manager  
As previously described, a behavior-based robotics architecture uses a collection of basic behaviors to perform robotic control, where behaviors are modular components that generate motor responses given some sensory stimulus which are run in a concurrent and distributed manner. Behavior-based robotic architectures are particularly well-suited to multi-vehicle control in dynamic environments due to scalability, decentralization of control, and tight coupling of rapid sensor readings to real-time path planning [31; 39]. The advantages inherent to a behavior-based robotics architecture make it highly well-suited to provide robust protections against jamming for an autonomous convoy. Using the Motor Schema approach as a starting point, we developed a novel behavior-based robotics architecture that uniquely takes advantage of the layered costmap system implicit in the ROS navigation stack. Our behavior-based architecture, henceforth referred to as the Behavior Manager, encompasses behavioral costmaps, behaviors, assemblages, and how they holistically interact. The following sections provide details on the general design of the Behavior Manager, along with the specific implemented system we are proposing for jamming mitigation of autonomous convoys.
# Emerging Roles of Toxin-Antitoxin Modules in Bacterial Pathogenesis
## 1 Toxin-Antitoxin Complexes  
Natural toxins are molecules produced by a wide variety of organisms--plants, animals and microorganisms. These toxins benefit the producing organism by, for example, inhibiting competing species, helping in defense, assisting predation, or promoting infection into a host species. Toxins produced by pathogenic bacteria against their hosts can be classified as either exotoxins or endotoxins. Exotoxins are generated and actively secreted whereas endotoxins are derived from the bacterial outer membrane and are not released until the bacterium dies [1; 2]. Bacteria also express highly potent antibiotics, bacteriocins and other compounds that kill or inhibit growth of other, often closely related, microbial species invading their niche [3]. These toxins are not harmful to the microorganisms which produce them. In addition to the preceding toxic factors, most bacterial and archaeal species encode toxin-antitoxin (TA) modules. These systems comprise toxin proteins that disrupt the producing cell's own molecular processes and cognate antitoxins that block this poisonous activity [4; 5; 6]. TA modules are distinctive as the toxic proteins are not secreted but instead act only within the producing cell. Thus, TAs are intracellular time bombs which, when activated, can temporarily disable their microbial host from the inside.  
Typically a pair of toxin and antitoxin genes is encoded within a single operon (Figure 1). The transcription and translation of the genes are tightly coupled to ensure proper stoichiometric quantities of both molecules [6]. The toxin invariably is translated into a protein, whereas the antitoxin functions either as a protein or as a small regulatory RNA. Toxins belonging to different families and exhibiting dissimilar modes of action show structural diversity. Nevertheless, most toxin proteins are comparatively small (<10 kDa) with a compact, globular fold whose core is composed of \(\beta\)-sheets [4; 7; 8; 9]. Protein antiotoxins exhibit less obvious common structural patterns. However, numerous protein antiotoxins possess regions that lack proper folding which makes them labile to protease digestion [4; 7; 8]. The general mechanism of TA action relies on differential _in vivo_ lifetimes of the two components, which are determined by their biophysical features. Toxin proteins exhibit longer lifetimes compared to the antidotes, which are prone to degradation by cellular nucleases or proteases. When the balance between the two factors is disrupted in response to diverse environmental or intracellular stimuli, the more resistant toxin is activated and reaches its cellular target causing bacteriostatic or bactericidal effects [6; 10]. These effects may be advantageous either by dampening cellular activity in response to stress cues, or by inducing the altruistic suicide of a fraction of cells that benefits the population as a whole.
## 3 Results
### Survival and Therapeutic Effect of Encapsulated Cells after Transplantation into Mice  
The preparation conditions of the cell-encapsulating alginate capsules were optimized based on the results of Figures 1-4. Specifically, a 1% alginate solution was prepared by dissolving 10 mg of sodium alginate in 1 mL of cell suspension at a density of \(5\times 10^{5}\) cells/mL. Then, 10 \(\upmu\)L of the 1% alginate solution was dropped into a 10% calcium chloride solution and gelled by immersing for 10 s. The survival rate and therapeutic effect of the encapsulated cells prepared by the optimized conditions were evaluated after transplantation into mice. Ten alginate capsules containing C3H10T1/2/Nluc cells at a density of \(5\times 10^{3}\) cells/capsule were intraperitoneallyally transplanted into BALB/c-nu/nu mice. As a control, \(5\times 10^{4}\) suspended C3H10T1/2/Nluc cells were intraperitoneallyally transplanted into BALB/c-nu/nu mice. The capsule group showed an almost constant level of relative light units in the blood for 40 days, whereas the cell suspension group showed a remarkable decrease in the level of relative light units from day 4 post-transplantation (Figure 5a). Accordingly, it can be suggested that the cell encapsulation in the alginate capsules prolonged cell survival post-transplantation. Furthermore, we evaluated the usefulness of the cell-encapsulated alginate capsules using insulin-secreting MIN6 cells. Fifteen alginate capsules containing \(5\times 10^{4}\) MIN6 cells (\(7.5\times 10^{5}\) cells in total) were intraperitoneallyally transplanted into STZ-induced diabetic mice. To establish a control group, \(7.5\times 10^{5}\) suspended MIN6 cells were intraperitoneallyally transplanted into mice. The suspension group had lower blood glucose levels than the sham-treated group, but no significant decrease was observed immediately after transplantation (Figure 5b). Conversely, the blood glucose levels in the capsule group were significantly decreased from day 1 post-transplantation. Accordingly, encapsulating the cells in alginate capsules improved the therapeutic effect of the insulin-secreting MIN6 cells.  
Figure 4: Effect of the cell density on the survival of encapsulated cells. Sodium alginate (10 mg) was dissolved in 1 mL phosphate-buffered saline (PBS) containing C3H10T1/2/Nluc cells at a density of \(1\times 10^{4}\), \(1\times 10^{5}\), \(5\times 10^{5}\), \(1\times 10^{6}\), or \(1\times 10^{7}\) cells/mL. Each 10 \(\upmu\)L alginate solution was immersed in 10% calcium chloride solution for 10 s to prepare alginate capsules. The cell-encapsulated alginate capsules were cultured, and the relative light units in the culture supernatant were measured every two days. Results are expressed as the mean \(\pm\) standard deviation (SD) (\(n\) = 4). The level of significance was measured using ANOVA following [PERSON]’s test compared to the \(1\times 10^{4}\) cells/mL group (\({}^{\circ}p<0.05\)).
## 4 Vehicular Ad Hoc Networks
### Routing  
Routing means the formation of a route between two vehicles called the source node and the destination node. In VANETs, routing protocols ensures that both two nodes can exchange their data packets with each other. These protocols are responsible for determining a suitable route between the source to the destination for sending data packets. Also, they are responsible to find an alternative route when failing routes. In such a case, if the routing path is not properly selected, the network performance will be severely weakened because existing links are constantly disconnected and new links are created. A routing approach must choose the most stable route to enhance network performance and reduce the need to rebuild existing paths [28; 29]. VANETs have specific features such as repeated change in topology and high-speed vehicles, which have challenged the routing process [30; 31]. In the following, we express some of these features: