esa-sceva/satcom-chunk-collection huggingface dataset:
# A Hybrid Linear Quadratic Regulator Controller for Unmanned Free-Swimming Submersible
## 6 Experimental Results
### Hydrodynamic Disturbances and Noisy Environmental Effect  
Previously, we have analyzed the non-linear response of pitch and heading control system which shows that our controller outperforms the other controllers. The environmental effects are unwanted disturbances produced due to the noise in sensors, variations in the depth, effects of object kinematics, and uncertainties of hydrodynamic coefficients due to the tides generated by the gravitational pull of the moon. Based on the aforementioned environmental disturbances, the performance of our controller is evaluated in the presence of generated disturbances (simulated profile), as shown in Figure 12. These disturbances are generated and simulated using MATLAB. For this, we have used band-limited white noise and Marine GNC toolbox in Simulink which help us in the generation of such a complex environment and to evaluate the behavior of our controller. Therefore, the UFSS control system is simulated with controllers under hydrodynamic disturbances as shown in Figures 13 and 14.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Controllers** & **Overshoot** & **Settling Time** & **Rise Time** & **Steady-State Error** \\ \hline PID & 20.1\% & 10.1 s & 1.22 s & 0\% \\ Lead & 19.8\% & 10.1 s & 1.25 s & 0.12\% \\ HLQR & 20.2\% & 9.83 s & 0.94 s & 0\% \\ \hline \hline \end{tabular}
\end{table}
Table 6: The response of pitch system with controllers.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Controllers** & **Overshoot** & **Settling Time** & **Rise Time** & **Steady-State Error** \\ \hline PID & 20.2\% & 10 s & 1.07 s & 0\% \\ Lead & 19.8\% & 10.3 s & 1.31 s & 0\% \\ HLQR & 19.9\% & 9.8 s & 0.792 s & 0\% \\ \hline \hline \end{tabular}
\end{table}
Table 7: The response of head system with controllers.  
As shown in Figures 13 and 14, the HLQR shows a minimal variation to the reference signal under hydrodynamic disturbances while the PID and lead-compensator controllers show a large variation to the reference signal. In other words, the PID provides a heightened response to the disturbances while the lead-compensator gives a nether and slower response in contrast to the reference signal. Based on this observation, it is possible to say that the HLQR tackles any disturbance limited to a system that varies with time while the HLQR is able to achieve a higher response.  
Figure 14: Heading angle control system with hydrodynamic disturbances.  
Figure 12: Adaptive structure for hybrid LQR.  
Figure 13: Pitching angle control system with hydrodynamic disturbances.  
other controllers (PID and lead-compensator) demand tuning and show more oscillation in handling ocean waves.
# A Review of Systems and Technologies for Smart Homes and Smart Grids
## Abbreviations  
\begin{tabular}{l l} ACORD & appliance coordination \\ ACORD-FI & appliance coordination with feed \\ AiSEG & artificial intelligence and smart energy gateway \\ BT & bluetooth \\ CPP & critical peak pricing \\ CPU & central processing unit \\ DD & dashboard or display device \\ DR & demand-response \\ D\&S & decision support tool \\ EC & European commission \\ EMU & energy management unit \\ GHGs & greenhouse gasses \\ GSM/GPRS & global system mobile \\ GUI & graphical users interface \\ HAN & home area network \\ HEMS & home energy management system \\ IP & internet protocol \\ IPPC & intergovernmental panel on climate change \\ ICTs & information and communication technologies \\ IBR & inclining block rates \\ IN & insteon \\ IPMR & intelligent power management rostrum \\ IWT & integrated wireless technology \\ LP & linear programming \\ MAC & medium access control \\ NAN & neighborhood area networks \\ OLM & optimum load management \\ OREM & optimization-based residential energy management \\ PC & physical component \\ PV & Photovoltaics \\ RC & remote control \\ RFID & radio frequency identification \\ REF & renewable energy source \\ RTP & real time pricing \\ S & sensor \\ SHMC & smart home micro-computers \\ SHS/HA & smart home systems/home automation \\ SM & smart meter \\ ToU & time of use \\ ZB & zigBee \\ ZW & Z-wave \\ WAN & wide area network \\ WSHAN & wireless sensor home area network \\ WSN & wireless sensor networks \\ xDSL & digital subscriber lines \\ \end{tabular}
## 1 Introduction
This solution, called the Priority-based Greedy heuristic (PG heuristic), is described in Section 3 in more detail. The work defines a VNF placement solution for edge computing networks that takes into account latency, bandwidth, and resource restrictions and also considers the virtualization overheads when instantiating VNFs as a parameter to be taken into consideration. To reduce this last cost, multi-tenancy technology is considered. Multi-tenancy, one of the benefits of the Software-as-a-Service (SaaS) model, is used to make multiple tenants (in this scenario, virtual network function requests) share the same software instance. As pointed out above, the combination of distributed micro data centers with cloud data centers allows the provision of network services that require strong low-delay requirements. For this reason, we consider it important to study the response of the solution of [13] to a significant and variable amount of this type of SFC request, an aspect that was not considered in the original work. Thus, the first contribution of this work is to study the adequacy and correct operation of the PG heuristic in this case. As a result, we identify a limitation of one of the algorithms defined by the PG heuristic that impacts the efficiency of the VNF placement solution when the percentage of SFC requests with strong delay restrictions increases. To solve this matter, we propose an alternative heuristic called the ModPG heuristic to give a solution to the VNF placement problem. Although both heuristics share the same optimization target, that is the reduction of the total substrate resource cost, our proposed ModPG heuristic solves the shortcomings of the PG solution. The percentage of SFC requests with strong low-delay restrictions was not considered as a relevant factor to be analyzed by [13]. In contrast, we introduce this factor as a vital value to show the improvement offered by our proposal. In this work, the PG solution and the ModPG solution are compared considering the presence of 1%, 15%, and 25% of SFC requests with strong low-delay restrictions. The results show that the ModPG heuristic optimizes the target cost similarly to the original proposal, and at the same time, it reduces the amount of non-allocated SFC requests. The remainder of the paper is organized as follows. Section 2 describes related works, and Section 3 presents some technical background. Section 4 presents the formulation of the VNF placement problem. Section 5 describes the shortcoming identified in the PG heuristic. In Section 6, we present our heuristic algorithms. In Section 7, simulation results are presented. Finally, Section 8 concludes the paper.
## 5 Discussion and Conclusions
This method is applicable in predicting the planned footprint of upcoming constellations and will pave the way for developing a more integrated sustainability framework that considers all three aspects of sustainability. The mass budget table for a LEO constellation, including propellant calculation, answers the research question, and it is presented in Section 2.3, with Starlink as a case study. The section is tailored according to the ESA LCI database. The reason for the need of Table 1 partitioning is discussed in detail in the average mass budget section. Using the method and taking Starlink version 1 constellation as a case study, the total environmental impact is estimated to be 76 kilo points. The results are represented in Table 6.
## 3 Results
### Removal of Debris and Smear Layer  
The assessment of debris and smear layer removal upon irrigation procedures was analyzed by SEM, and both the representative images and the number of samples indexed for each score are presented in Figure 2. In the manual group, regardless of the apical size, the entire root canal walls presented superficial debris and a smear layer, without any visible openings of the dentinal tubules. After ultrasonic activation, root canals at 35/.06 display the minimal presence of debris and a thin smear layer covering the walls, with a low frequency of visible/patent dentinal tubules. In root canals at 50/.06, the presence of debris and smear layer was minimal, and the identification of patent dentinal tubules was substantial. As for sonic activation, root canals prepared at the smaller apical size presented minimal to moderate debris and smear layers, with many dentinal tubules exposed. In contrast, root canals with larger apical size exhibited a reduction in superficial debris, while the smear layer persisted at a minimal to moderate level, with numerous identifiable dentinal tubules. Overall, specimens with an apical size of 50/.06 that underwent sonic or ultrasonic activation showed reduced debris and smear layers compared to those subjected to manual irrigation.
## 3 WSAN for Control Applications
However, the use of dynamic routing protocols and random MAC protocols (e.g., CSMA/CA), as well as the mobility of nodes, makes the WSAN-induced delay time-varying and unpredictable. The challenge here is how to guarantee the delay is sufficiently small and deterministic with small jitter so that it will not significantly degrade the control performance. As mentioned in Section 2, WSAN-induced time-varying delay has been addressed in [17,18]. In the recent work by [PERSON] and colleagues [19-21], a real-time queuing protocol has been developed that can be used to tackle the second design challenge of WSANs. Therefore, in this paper, we concentrate our attention on the first design challenge concerning the reliability of WSANs. The existence of mobile nodes in the system undoubtedly makes this task even more difficult. In the following, we will restrict our description to WSANs with the first type of architecture as shown in Fig. 1, since it is more resource-efficient and is more representative of the next generation of sensor networks. It is, however, noteworthy that our design method is applicable to a wide range of WSANs with arbitrary architectures. Typical examples of (ongoing) real-world application scenarios of the considered WSANs include the pollution source location problem and the fire in a road tunnel scenario where mobile robots must be controlled in a WSAN to accomplish certain jobs [33]. When illustrating our approach, we will exploit a high-level abstraction of the WSANs for various application setups in order to maintain the application independency and wide applicability of our solution.
# The Limits of SEMA on Distinguishing Similar Activation Functions of Embedded Deep Neural Networks
## 5 Identification Using SEMA
### Discussion  
We were able to distinguish the eight activation functions. We acquired the same peaks for the Sigmoid function, Tanh function, softmax function, and rectified linear unit (ReLU) function as the work by [PERSON] et al. (2016). However, these popular activation functions become very difficult to distinguish with the similar activation functions. The Sigmoid function and Swish function are difficult to distinguish by SEMA except when processing the input 0. The identification by SEMA was performed through observing the differences in peak patterns coming from the variability in the operations of the functions. We believe the operations and peaks of the traces have a relation, allowing SEMA to distinguish the activation functions, but the limit of SEMA is that difference in values will be ignored. From the results, it can be noted that using activation functions with the same operations with different values would be the safest way to make a black box neural network, one example being the Sigmoid function or Swish function.  
There are neural networks implemented on different platforms, such as a GPU platform or a FPGA platform. We believe the SEMA-based approach and methodology could be applied to different platforms, such as these similar microcontroller platforms, because side-channel analysis has been demonstrated on these platforms in several previous works ([PERSON] et al., 2016; [PERSON] et al., 2016; [PERSON] et al., 2016). One work by [PERSON] et al. reverse-engineered neural network designs, such as the number of layers and neurons, as well as types of activation functions on GPUs through SEMA ([PERSON] et al., 2016). When we carefully observe the traces recovered by [PERSON] et al., we can see the difference in peak patterns through different types of  
Figure 10: Processed Peak Patterns of Fast Sigmoid Function.  
activation functions. Though the peaks are not necessarily the exact same as the ones on our microcontroller, by using the same method to extract the peaks and pre-characterize them, we believe the peaks can be used to distinguish activation functions more clearly with reasoning than with just computation time, as done in their work.  
The proposed SEMA attack can be combined with the full reverse-engineering of the neural network in [15]. By making a template and pre-characterizing the operations in the EM trace, activation functions can be distinguished with SEMA. Using the SEMA-based identification to replace the timing-based activation function identification, we believe the reverse-engineering can become a more versatile attack.
## 2 Material and Methods
### Meshing  
Due to the topological complexity of the FE human hand and index finger model presented in this study, tetrahedral SOLID187 10-node elements were chosen for mesh sensitivity analysis. Tetrahedral elements have frequently been used in finite-element-based studies involving complex human geometry, for example, for FEM analysis of the knee joints [30], shoulder bones [31], tibia [32], and femur [33]. For simulation of articular contacts, 10-node quadratic tetrahedral elements exhibit good performance in terms of computational cost and accuracy [34]. Therefore, tetrahedral elements were used and mesh defeaturing was turned off to ensure proper bonded contact between the hand bone and soft tissues. Mesh independency analysis was also performed under a constant load using ANSYS Static Structural. Initially, a coarse mesh was used to perform a quasi-static analysis. An increasingly fine mesh was then employed to achieve mesh convergence. The final mesh, which offers a balanced tradeoff between performance and accuracy, had  
\begin{table}
\begin{tabular}{c c} \hline \hline  & **Bone** \\ \hline Young’s Modulus & 17 GPa \\ \hline Poisson’s Ratio & 0.3 \\ \hline Density, \(\rho\) & 2000 kg/m\({}^{3}\) \\ \hline \multicolumn{2}{c}{**Skin**} \\ \hline Material Parameter, \(\alpha_{1}\) & 9 \\ \hline Material Parameter, \(\mu_{1}\) & 0.11 MPa \\ \hline Compressibility, \(D_{1}\) & 2 MPa\({}^{-1}\) \\ \hline Density, \(\rho\) & 1040 kg/m\({}^{3}\) \\ \hline \multicolumn{2}{c}{**Joints**} \\ \hline Stiffness & 142 N\(\cdot\)mm/\({}^{\circ}\) \\ \hline Damping & 2.4 N\(\cdot\)mm\(\cdot\)s/\({}^{\circ}\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Material properties of human hand bone and soft tissue.  
274,247 nodes and 164,813 elements for the full hand model. Additional refinement was added at point(s) of impact for Explicit Dynamics simulations.
## 3 Disturbance Rejection Dynamic Inverse Control
### Disturbance Observer Enhancement
\[\|e_{4_{1}}\| \leq\|e^{-(t-t_{0})L}\|\|e_{4_{1_{0}}}\|+\int_{t_{0}}^{t}\|e^{-(t- \tau)L}\|\underset{t_{0}\leq\tau\leq t}{sup}\|\dot{d}_{1}(\tau)\|\,d_{\tau} \tag{38}\] \[\leq\kappa_{1}e^{-\kappa_{2}(t-t_{0})}\|e_{4_{1_{0}}}\|+\int_{t_{ 0}}^{t}\kappa_{1}e^{-\kappa_{2}(t-t_{0})}d_{\tau}\underset{t\geq t_{0}}{sup}\| \dot{d}_{1}(t)\|\] \[\leq\kappa_{1}e^{-\kappa_{2}(t-t_{0})}\|e_{4_{1_{0}}}\|+\tfrac{ \kappa_{1}}{\kappa_{2}}\underset{t\geq t_{0}}{sup}\|\dot{d}_{1}(t)\|\]  
Thus, the ultimate bound of the estimation error is given by  
\[\underset{t\rightarrow+\infty}{limsup}\|e_{4_{1}}\|\leq\frac{\kappa_{1}}{ \kappa_{2}}\underset{t\geq t_{0}}{sup}\|\dot{d}_{1}(t)\| \tag{39}\]  
where \(\kappa_{1}\) and \(\kappa_{2}\) are positive constants decided by the Hurwitz matrix \(-L\). Under Assumption 1, it is noted from Equation (38) that the zero-input response decays to zero exponentially fast, while the zero-state response is bounded for every bounded input [27; 47]. Additionally, Equation (39) shows that the estimation error can be finally regulated into an arbitrarily small neighborhood of zero with sufficiently small \(\kappa_{1}/\kappa_{2}\). To do this, positive and sufficiently large elements of the gain matrix in Equation (36) are required to achieve faster observer dynamics than that of the disturbance [25; 27]. Similarly, recalling the Hurwitz property of \(-B_{1}\), the tracking error can be expressed as  
\[\|e_{\kappa_{1}}\| \leq\|e^{-(t-t_{0})B_{1}}\|\|e_{\kappa_{1_{0}}}\|+\int_{t_{0}}^{t }\|e^{-(t-\tau)B_{1}}\|\underset{t_{0}\leq\tau\leq t}{sup}\|e_{4_{1}}(\tau)\| \,d_{\tau} \tag{40}\] \[\leq\kappa_{3}e^{-\kappa_{4}(t-t_{0})}\|e_{\kappa_{1_{0}}}\|+\int _{t_{0}}^{t}\kappa_{3}e^{-\kappa_{4}(t-t_{0})}d_{\tau}\underset{t\geq t_{0}}{ sup}\|e_{4_{1}}(t)\|\] \[\leq\kappa_{3}e^{-\kappa_{4}(t-t_{0})}\|e_{\kappa_{1_{0}}}\|+ \tfrac{\kappa_{3}}{\kappa_{4}}\underset{t\geq t_{0}}{sup}\|e_{4_{1}}(t)\|\]  
From this, it follows that:  
\[\underset{t\rightarrow+\infty}{lim}\underset{t\rightarrow+\infty}{sup}\|e_{ \kappa_{1}}\|\leq\frac{\kappa_{1}\kappa_{3}}{\kappa_{2}\kappa_{4}}\underset{ t\geq t_{0}}{sup}\|\dot{d}_{1}(t)\| \tag{41}\]  
where \(e_{\kappa_{1,0}}=e_{\kappa_{1}}(t_{0})\) denotes the initial value of tracking error and \(\kappa_{3},\kappa_{4}\) are positive constants decided by \(-B_{1}\). The effects of the lumped disturbance on the tracking error is bounded under Assumption 1 and can be mitigated with sufficiently small \(\kappa_{1}\kappa_{3}/\kappa_{2}\kappa_{4}\). Namely, it is beneficial to reject the disturbance with larger gains in both the bandwidth matrix \(B_{1}\) and the gain matrix \(L\).
## 1 Introduction
### Impact of Artificial Intelligence on Healthcare  
Artificial intelligence (AI) algorithms are expected to transform the future of healthcare, with applications in medical imaging analysis, drug discovery, remote patient care, rehabilitation, and diagnostics [7]. AI provides an extensive scale of optimized models for predicting diagnoses from medical images. For example, scientists at Google [8] exploited 128,175 retinal fundus images to diagnose diabetic retinopathy. Other examples include radiographs [9], magnetic resonance imaging scans [10; 11], and ultrasound images [12] where deep learning (DL), a sub-group of machine learning (ML), was applied for reliable medical diagnostics. Likewise, drug discovery from the genomics data is becoming popular and revolutionizing pharmacology [13]. The intent is to identify the most appropriate medication for individuals, focusing on those that efficiently target specific proteins. Some examples include predicting the drug target interaction in [14] and androstane receptor using DeepSnap models [15]. Moreover, the correlation of stress with asthma has been explored in [16], demonstrating potential AI application in understanding asthma triggers. The recent AI technology, i.e., generative pre-trained transformer (GPT) models that are advanced versions of transformer and natural language processing (NLP) algorithms, has proven helpful for physicians in creating personalized rehabilitation plans for elderly patients [17]. However, it has some limitations, i.e., lacking information sources and bioethical considerations. Similarly, memory consumption, performance, and lack of trust in patients and professionals are common concerns.
# Designing Automated Logistics Warehouse Stackable Bidirectional Infinite-Loop Modules
## 6 Conclusions  
In this study, we proposed an automated logistics warehouse model incorporating stackable bidirectional infinite-loop modules to address the changing demands and challenges of the logistics market. Through simulations, we observed a time efficiency improvement ranging from approximately 37.54 to 73.22% depending on the quantity of logistics processed, when compared to models without the stackable bidirectional infinite-loop modules. Even in simulated bottleneck scenarios, typical of logistics automation warehouses, the proposed model exhibited time efficiencies of approximately 38.39 to 73.31% depending on the quantity of logistics processed. These results clearly demonstrate the superior processing capabilities of the model with stackable bidirectional infinite-loop modules compared to conventional logistics automation warehouses.  
The introduction of this automated logistics warehouse model with stackable bidirectional infinite-loop modules is expected to offer solutions to the challenges faced by current automated warehouses. This includes maximizing storage capacity through enhanced space utilization, faster processing speeds owing to swift incoming and outgoing logistics, resolving workforce shortages, and improving land usage efficiency. Moreover, the application of these stackable bidirectional infinite-loop modules in vertical farms within the context of smart farming holds promising potential. Additionally, it is expected to contribute toward a reduced workload owing to the reduction in labor requirements as a result of the enhanced efficiency in logistics movement.  
For future research, it is crucial to focus on the hardware design of the system incorporating stackable bidirectional infinite-loop modules and ensure its validation in various fields such as logistics and vertical farming. Through validation, potential avenues for increasing energy efficiency, strengthening sustainability, and enhancing overall environmental friendliness of the entire system should also be explored.  
Conceptualization, K.M. and D.L.; software, K.M.; validation, D.L.; resources, K.M. and D.L.; writing--original draft preparation, K.M.; writing--review and editing, K.M. and D.L.; supervision, D.L. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Korea Institute of Energy Technology Evaluation and Planning (KETEP) grant funded by the Republic of Korea government (MOTIE) (No. 2022400000080).  
Not applicable.  
The data presented in this study are available on request from the corresponding author. The data are not publicly available due to ongoing research collaborations and publication commitments.  
The authors declare no conflict of interest.