esa-sceva/satcom-chunk-collection huggingface dataset:
# Adaptive Deterministic Vibration Control of a Piezo-Actuated Active-Passive Isolation Structure
## 3 Design of Adaptive Active Vibration Controller  
### Design of the Inner-Loop Central Controller  
As shown in Figure 3, the feedback controller \(K\) without the \(Q\) parameter is the inner-loop central controller. The controller \(K\) is designed as an LQG controller to shape the band-limited local loop of the closed-loop system. Let \(w_{b}(k)\) represent the band-limited signal near the system natural frequency, thus the reference model can be expressed under the white noise signal \(w_{n}(k)\) as:  
\[\Sigma_{b}\;:\;\left\{\begin{array}{l}x_{b}(k\;+\;1)\;=\;A_{b}x_{b}(k)\;+\; B_{b}w_{n}(k),\quad x_{b}(0)\;=\;x_{b,0}\\ w_{b}(k)\;=\;C_{b}x_{b}(k)\end{array}\right. \tag{3}\]  
Correspondingly, system (1) can be extended to  
\[\Sigma\;:\;\left\{\begin{array}{l}\widetilde{x}(k\;+\;1)\;=\;\widetilde{A} \widetilde{x}(k)\;+\;\widetilde{B}u(k)\;+\;\widetilde{E}_{x}w(k),\quad \widetilde{x}(0)\;=\;\widetilde{x}_{0}\\ e(k)\;=\;\widetilde{C}_{e}\widetilde{x}(k)\;+\;\widetilde{E}_{e}w(k)\;+\;v(k) \end{array}\right. \tag{4}\]  
\[\text{where }\widetilde{x}(k)\;=\;\left[\begin{array}{l}x(k)\\ x_{b}(k)\end{array}\right]\text{, }\widetilde{A}\;=\;\left[\begin{array}{l}A\; \;\;\;\;0\\ 0\;\;\;A_{b}\end{array}\right]\text{, }\widetilde{B}\;=\;\left[\begin{array}{l}B\\ 0\end{array}\right]\text{, }\widetilde{E}_{x}\;=\;\left[\begin{array}{l}0\;\;\;\;0\\ 0\;\;\;B_{b}\end{array}\right]\text{, }\widetilde{C}_{e}\;=\;\left[\begin{array}{l}0\;\;\;\;0\\ 0\;\;\;B_{b}\end{array}\right]\text{, }\widetilde{C}_{e}\;=\;\left[\begin{array}{l}0\;\;\;\;0\\ 0\;\;\;\;B_{b}\end{array}\right]\text{, }\widetilde{C}_{e}\;=\;\left[\begin{array}{l} 1\;\;\;0\\ 0\end{array}\right]\text{, }w\;=\;\left[\begin{array}{l}w_{d}(k)\;\;\;w_{n}(k)\end{array} \right]^{T}\text{.}\]  
The observer-based state feedback controller for the system (4) can be given as:  
\[K:\left\{\begin{array}{l}\mathfrak{x}(k+1)=\widetilde{A}\mathfrak{x}(k)+ \widetilde{B}u(k)+L(\mathfrak{y}(k)-y(k)),\quad\mathfrak{x}(0)=\mathfrak{x}_{0} \\ u(k)=F\mathfrak{x}(k),\end{array}\right. \tag{5}\]  
where \(\mathfrak{x}(k)\) is an estimate of the state vector \(\widetilde{x}(k)\), \(F\) represents the state feedback gain, and \(L\) represents the observer gain.  
The optimal state feedback gain \(F\) is obtained according to the quadratic optimal control method, and the observer gain \(L\) is determined by the Kalman filter method. Then the band-limited local loop of the closed-loop system will be shaped by selecting the appropriate weight parameters of the LQG controller.
## 3 System Model
### Requirements
The most promising feature of the future architecture is the ability to add new functions to the vehicle by simply adding a software driver, very similar to the case of adding a new application to a mobile phone. Software is transforming capabilities in the vehicle but also creating development challenges for OEMs. OEMs have to manage the software complexity introduced by new and advanced functionalities; distinguish which software is running into an ECU regardless of the vehicle models, markets, and activation states; as well as provide dynamic software updates at any time. To address such automotive demands, we aim to design a software update scheme with the following requirements:  
* **Requirement 1**: The scheme shall allow detection of any tampering with the software image. * **Requirement 2**: The scheme shall allow detection of any incompatibilities between the logically or physically dependent software images in the vehicle. * **Requirement 3**: The scheme shall work with any frameworks or platforms supporting multiple binaries programming using digital signatures. The scheme shall support different types of hash algorithms as well as different signature mechanisms. * **Requirement 4**: The scheme shall allow efficient addition or removal of software clusters. The vehicle should be able to receive requests to add a new software cluster or remove an existing one. Thus, the scheme shall enable efficient generation and secure storage of the signature. * **Requirement 5**: As ECUs might be heterogeneous in terms of hardware, computation capabilities, and memory storage, the scheme shall make it possible to induce insignificant overheads in the lightweight ECUs with limited memory and computation resources on the installation of additional images. * **Requirement 6**: The scheme may be used for intra-ECU and inter-ECU distribution of software clusters. Dependant software clusters can be located in the same physical ECU. However, it is possible they are physically running in different ECUs. In this case, it should be possible for the ECU to validate the signature even it doesn't have access to complete software clusters. This enables flexibility to move a software cluster from one ECU to another ECU while keeping the dependency among the software clusters. * **Requirement 7**: The scheme shall use one key pair per ECU to generate and verify the signature regardless of how many software clusters are used per ECU.
## 3 Flight Maneuver Recognition
### Flight Maneuver Recognition  
Despite the preceding preprocessing, the regularity of the flight parameter data persists poorly, posing challenges in distinguishing between different maneuvers. Identifying maneuver segments is imperative, relying on the characteristics exhibited by flight parameter data of various maneuver types and comparing these traits to ascertain the corresponding maneuver segments for the current data segment. An illustration of the general maneuver recognition process is depicted in Figure 3.  
Generally, aircraft maneuvers are classified into three categories: vertical plane, horizontal plane, and spatial maneuvers. The maneuver recognition algorithm based on the time series of significant points, utilizing the trend states of trajectories projected onto the horizontal and vertical planes, which can be referred to our previous proposed Sequence Important Point-Based Method. The specific steps are as follows:  
Step 1: Divide the flight parameters into multiple segment sequences with fixed lengths, and then project the trajectory of each sequence onto the horizontal plane.  
Step 2: Apply certain merging rules, utilizing the piecewise linear representation and perceptually important point algorithm (PLR-PIP) to segment the trajectory projection, identify the sequence trend, and merge adjacent trend sequences with the same state.  
Step 3: Project the merged trend sequences onto the vertical plane; then, the PLR-PIP algorithm is used to segment the trajectory projection, identify the sequence trend, and subdivide the trend sequences by subdivision rules.  
Step 4: Superimpose the trend states of the two plane projections to obtain the basic aircraft maneuvers, as illustrated in Figure 4.  
Figure 3: Flowchart for a general maneuver recognition method.  
Details can be found in our previous work [26].
# QoS-Guaranteed Radio Resource Management in LTE-A Co-Channel Networks with Dual Connectivity
## 5 Conclusions and Future Works  
In this work, we discussed the development of spectrum utilization in the future. For heterogeneous network research, we designed how to integrate dual connectivity, cell selection, resource allocation, and e-ICIC in the co-channel networks. On the other hand, we studied UEs with different QoS requirements and traffic characteristics, such as voice traffic (low data transfer rate) and video traffic (high data transfer rate). In the resource allocation mechanism, we propose the MTS scheduling to meet the QoS requirement, considering the resource integration across the base stations to enhance the data transfer rate. In the cell selection mechanism, we propose the HCI scheme to consider not only the signal quality received by the UE, but also the remaining RBs of each base station, and further achieve the load balancing between the base stations. In the e-ICIC mechanism, we propose the QD-ABS scheme to adjust the ABS ratio dynamically according to system feedback.  
The ultimate goal of the proposed mechanisms is to improve the QoS satisfaction ratio of UEs. Our simulation results showed that our MTS scheduler was able to achieve 31.44% higher rate than the Proportional Fairness scheduler; our HCI cell selection scheme yielded 2.98% higher rate than the SINR cell selection scheme; the QoS satisfaction ratio of our Q-learning-based dynamic ABS scheme was 4.06% higher than that of the static ABS scheme. Finally, by integrating the mechanisms of dual connectivity, CRE, and ABS, the QoS satisfaction ratio of cell edge UEs could be improved by 10.76% as compared to the traditional approach.  
We are expecting to see some small-scale commercial deployments for the fifth generation (5G) networks by some leading operators in 2019 worldwide. The 5G air interface, called new radio (NR), is expected to interrupt with different wireless technologies where UEs will have multiconnectivity capabilities. In particular, in the early stage of 5G deployment, interworking with LTE-A based on dual connectivity will be necessary to boost the deployment and has been proposed by 3 GPP as a 5G operational requirement [2,36]. Extending the dual connectivity to multi-connectivity in 5G will become an important technique and require further study [37].  
Conceptualization, R.-H.H. and M.-C.P.; methodology, R.-H.H. and K.-C.C.; software, K.-C.C.; validation, R.-H.H. and [PERSON]; formal analysis, [PERSON]-[PERSON] and [PERSON]-[PERSON]; investigation, [PERSON]-H.H. and [PERSON]; resources, R.-H.H.; data curation, K.-C.C.; writing--original draft preparation, K.-C.C.; writing--review and editing, R.-H.H. and [PERSON]-[PERSON]; visualization, K.-C.C.; supervision, R.-H.H.; project administration, R.-H.H.; funding acquisition, R.-H.H.  
This research was funded by Ministry of Science and Technology, Taiwan, grant number MOST 105-2221-E-194-03 and MOST 106-2221-E-194-021-MY3.  
The authors declare no conflict of interest.  
Figure 14: Satisfaction ratio of the scheme that integrates dual connectivity with cell range expansion (CRE) and ABS.
# Practical Approach for Assessing Wetting-Induced Slope Failure
## 2 Site Overview of Cimanggung Slope Failure  
On 9 January 2021, catastrophic slope failures occurred at Cihanjuang village, located at Cimanggung, Sumedang, West Java. It is interesting to note that, for more than 20 years, slope failure never occurred in this area. SBG Housing (_Perumahan SBG_) is located at the crown of the slope while Daud housing (_Perumahan Daud_) is located at the body of the slope, as shown in Figure 1.  
According to UInspire [32], 32 people died, 3 people were heavily injured, 22 people were lightly injured, while 8 people were missing. A total of 24 houses were completely destroyed, 5 houses were heavily damaged, 1 school, and 1 mosque were destroyed. Figure 2 shows the example of the destruction caused by the slope failure. The housing complex has been there for more than 25 years without any effect from slope failure which makes the citizens wonder at the causes of slope failure.  
Figure 1: Post-failure slope conditions.  
According to the witnesses, slope failure occurred after more than four hours of heavy rainfall which might be the major cause of the slope failure. According to several ground stations around the slope area [32], rainfall during the slope failure was as follows:  
1. [label=()]
2. Jatinangor district (143 mm/day);
3. UNPAD Jatinangor (99 mm/day);
4. Tanjung Sari (104 mm/day);
5. AWS Cikancung (69.8 mm/day).  
The data shows some variability and, as hourly rainfall data is not available at the site, it is difficult to accurately determine the actual infiltration rate and duration.  
Another aspect which is worth considering is the possibility that drainage household capacity was exceeded. According to the site observation, household drainage, which is commonly located in front of the citizen housing, which is shown in Figure 3, is designed for 5 years of rainfall which appears to be insufficient. Hence, water floods and flow from the housing complex to the slope triggered the first landslide and damaged the drainage pipe located at the crown of the slope, as indicated in Figure 4. It is important to note that the first landslide was located below the drainage pipe. Hence, it will be evaluated whether the slope failure was only due to the rainfall or also due to the failure of the drainage system which caused overtopping or flooding at the crown of the slope.  
In order to conduct proper back analysis, accurate geometry of the sliding plane or the post-failure slope geometry is very important. However, obtaining post-failure geometry might be hazardous as the site might remain unstable for the operator. Due to the development of the photogrammetry technique, it is now possible to generate a 3D slope model from a series of overlapping photographs by using aerial reconnaissance using an unmanned aerial vehicle or UAV [33; 34; 35]. By using UAV, post-geometry slope failure can now be determined accurately from a safe distance within a day.  
Figure 2: Destruction caused by the slope failure ((a) shows destruction of house located at the edge of the slope; (b) shows buried house at the bottom of the slope).
## 4 Progress of Data Processing Modules for Solar PV Monitoring System
### Arduino Based Module
Although the technology of Arduino boards possesses several advantages such as low cost, adaptability to various operating systems such as Linux, Windows and Macintosh, easy and flexible programming environment, etc., it suffers from various limitations too. Firstly, it suffers from a limited bit resolution of up to 10 bits. Secondly, for complex and advanced research, more sophisticated platforms are chosen over Arduino boards as the latter is not capable of handling complex data consisting of several processes at once [54].
## 2 Materials and Methods  
### Materials  
Dulbecco's Modified Eagle Medium (DMEM) powder with high glucose (containing L-glutamine, pyridoxin hydrochloride, sodium pyruvate, and without sodium bicarbonate) and DMEM liquid were purchased from Sigma (St. Louis, MO, USA). Fetal Bovine Serum (FBS), Trypsin-Ethylene Diamine Tetra Acetate (T Trypsin-EDTA) and penicillin-streptomycinycin were obtained from Gibco BRL (California, United States); calcium chloride dihydrate (CaCl\({}_{2}\cdot\)2H\({}_{2}\)O), sodium bicarbonate, sodium sulphite, dimethyl sulphoxide (DMSO) and thiazolyl blue tetrazolium bromide (MTT) reagent were purchased from Sigma-Aldrich (St. Louis, MO, USA). The Hs-PRKCA\({}_{5}\) Flexitube siRNA was purchased from Qiagen (NM\(\_{002737}\), Genelgobe ID-Sl00301308) and plasmid encoding PTEN tumour suppressor gene was from 'Addgene' (Watertown, MA, USA). The antibiotic, ampicillin from Fisher Scientific (Loughborough, Leicestershire, UK), Luria Bertani (LB) agar from Merck (St. Louis, MO, USA) and LB broth were procured from Laboratories CONDA (Madrid, Spain). Two human breast cancer cell lines, MDA-MB-231, MCF-7, and one mouse breast cancer cell line, 4T1, were obtained from ATCC (American Type Culture Collection). For Western blotting, Tween-20 and Pierce ECL Western blot detection reagents were purchased from Bio-Rad Laboratories, Inc. (Hercules, CA, USA). DTT (Bio-Rad), bovine serum albumin, BSA protease inhibitor and phosphatase inhibitor cocktail were obtained from Sigma (St. Louis, MO, USA).  
USA). Monoclonal IgG primary antibodies raised in rabbit for phospho-AKT, total AKT, GAPDH, and the horseradish peroxidase (HRP)-conjugated secondary goat anti-rabbit IgG antibody were from Cell Signaling Technology, Inc. (Beverly, MA, USA).
# Key Considerations for Real-Time Object Recognition on Edge Computing Devices
## 3 Key Considerations of Object Recognition on Edge Computing
### Deep Learning Frameworks for Edge Implementation
Some of the well-known deep learning frameworks are vendor-specific frameworks, such as TensorRT (NVIDIA), OpenVINO (Intel) [60], and VitisAI (Xilinx) [12]. If the flexibility in development or cross-compatibility to several hardware becomes a concern, then an ONNX framework can provide a solution. It provides cross-platform compatibility by acting as an open standard for representing deep learning models. This compatibility enables the seamless deployment and interoperability of models across various deep learning frameworks and hardware platforms [23]. For example, ONNX defines a standardized format (.onnx) for representing deep learning models, independent of the framework in which they were trained. Models from popular frameworks such as TensorFlow, PyTorch, and Keras can be converted to ONNX format. ONNX Runtime also supports various execution providers, which enable hardware-specific optimizations while maintaining compatibility; for example, CUDA execution provides optimized inference on NVIDIA GPUs, and OpenVINO execution provides model optimization in Intel-based edge devices. Finally, for FPGA implementation, there are several well-known frameworks, such as Vitis AI, OpenCL [61], and FINN, that enable the efficient deployment of AI models on FPGA platforms by leveraging hardware-specific optimizations and parallel processing capabilities. Vitis AI, developed by Xilinx, offers pre-built tools and libraries for deploying optimized models on Xilinx FPGAs, supporting frameworks like TensorFlow and PyTorch while providing model pruning, quantization, and real-time performance [62]. OpenCL provides a standardized programming interface for creating portable FPGA implementations, allowing developers to write high-level, parallel code that is compiled into FPGA-compatible logic, making it versatile for custom AI applications [11]. FINN, a framework from Xilinx, focuses on ultra-low-latency, quantized neural networks, generating FPGA-optimized designs tailored for resource-constrained tasks like edge computing [63]. These frameworks make FPGAs accessible for deep learning, offering high energy efficiency, real-time processing, and customizable solutions for AI workloads in applications like autonomous systems, healthcare, and robotics. A summary of deep learning frameworks for edge implementation is shown by Table 3.
## 2 Materials and Methods
### Experimental Design  
Twenty-eight experimental samples were prepared using sterile Eppendorf tubes 20 mm in length and filled with TSA for 10 mm (Figure 1A). The remaining space simulated mean depth of endodontic cavity access preparation from dental cusps to pulp chamber floor in molars, as reported in recent studies using Cone-beam computed tomography [29]. As temporary filling material was used Plastor() (Ghimas, Bologna, Italy), a premixed temporary filling material that contains mainly zinc oxide (40-50%), calcium sulphate (20-30%). As spacers were used sterile cotton pellets (Medirel, Agno, Switzerland) or PTFE pellets (TDV Dental, Pomerode, Brazil). Samples were divided in 7 groups (N = 4 vials/group): group A (Plastor()), group B (Plastor() + cotton pellet), group C (Plastor(r) + cotton pellet soaked of m-cresyl acetate), group D (Plastor(r) + cotton pellet soaked of eugenol), group E (Plastor(r) + PTFE pellet), group F (positive control), and group G (negative control).  
Sterile spacers were gently immersed into the TCA, inside the Eppendorf tubes. In group C and D cotton pellets were soaked in 0.1 mL of m-cresyl acetate or eugenol immediately before the insertion. Plastor(r) was applied in a bulk technique in the access cavity, simulating a Class 1 cavity, with sterile instruments and subsequently condensed with a plugger to obtain a thickness at least of 5 mm (Figure 1B). The excess material was removed with a sterile cotton pellet lightly dampened with sterile saline to obtain a smooth surface. Subsequently, filling material was left in contact for 60 min with a sterile cotton pellet soaked with sterile saline solution to obtain its hardening (Figure 1C).  
Preparation of the samples was aseptically performed by the same operator.  
In the vials of group F (positive control) were filled with 20 uL of undiluted bacterial suspension of _S. mutans_ before application of filling material, and in group G (negative control) were added 20 uL of sterile broth. All prepared vials were sterilized on the outer surface using a hypochlorite solution and were individually immersed in tubes containing 5 mL of a suspension of _S. mutans_ (10\({}^{5}\) CFU/mL) previously prepared and then incubated at 37 \({}^{\circ}\)C (Figure 1D).  
Two Eppendorf tubes were collected for each group, positive and negative control after 5 (T5) and 10 (T10) days of incubation. After disinfection of the external surface using 5.25% sodium hypochlorite, the vials were opened aseptically and their content was used for the detection and in vitro isolation of _S. mutans_ by real time PCR and inoculation on TSA, respectively.
## 4 Experiments and Results
### Evaluation Metrics  
Accuracy and loss value are the two most common evaluation indexes for image classification. The accuracy represents the proportion of correctly classified samples in all samples, which is the evaluation index that most directly reflects the performance of the classification model. It is formulated by Equation (8):  
\[\text{Accuracy}\,=\,\frac{t}{T} \tag{8}\]  
where, \(t\) is the number of samples correctly classified, and \(T\) is the total number of samples.  
In this study, the cross-entropy loss function was used to quantitatively evaluate the difference between the predicted value and the real value. Through the calculation of the loss function, the parameters of our model were updated. It is formulated by Equation (9):  
\[\text{Loss}=\frac{1}{\text{N}}\sum_{i}\text{L}_{i}=-\frac{1}{\text{N}}\sum_{i }\sum_{c=1}^{\text{M}}y_{ic}\log(p_{ic}) \tag{9}\]  
where M is the total number of types. \(y_{ic}\) is the indicator variable, and if the type is as same as the type of the sample \(i\), \(y_{ic}\) is 1, otherwise it is 0. \(p_{ic}\) is the prediction probability of the sample \(i\) belonging to the type \(c\).