esa-sceva/satcom-chunk-collection huggingface dataset:
# Security Architecture for Cloud-Based Command and Control System in IoT Environment
## 3 Security Requirements of Cloud-Based C4I System  
In this section, we explain the derived security requirements for the cloud-based C4I system by referencing the security requirements of the Korean military information system and the cloud-related security requirement of the DoD and Korea institute. We derived security requirements for countermeasures against security threats related to the cloud environment in the previous work, that must be considered when the current military environment adapts cloud computing. In order to derive the new security requirements, we referenced the security requirements identified by the 'Security Controls Baseline (FedRAMP)' [5], 'Cloud Computing Security Requirements Guide (DISA)' [6], 'Security Certification Guide for Cloud Service (Korea Internet and Security Agency)' [14], 'Security Requirements for Server Virtualization System (Telecommunications Technology Association)' [15] and 'Security Guidelines of National and Public Institution for Cloud Computing (National Intelligence Service)' [16]. The derived security requirements are divided into five fields and contain 28 items, as shown in Figure 2. The fields include virtual security, data protection, network security, access control and risk management. Security requirements related to virtualization technology require factors for virtual resources, such as virtual machines, virtual storage, virtual servers and virtual software. Virtualization security applies to servers, PCs and applications. Server virtualization security includes hypervisor security and public server security, and application security includes virtual software security. Public server security demands the requirements of physical and technical security measures for websites to provide a virtual resource and a server to distribute virtual software, such as applications. Virtual software security is a requirement where the cloud computing service provider must provide a virtual environment comprising a clear source, a distribution channel and manufacturers. In addition, the three fields typically include malware control, interface security and API security in a virtual environment. Security requirements for the resource management of cloud computing systems include identifying information assets such as information systems and information security systems used in cloud computing, management by security level, and continuous monitoring. In addition, it is necessary to manage the creation, modification and withdrawal of virtual resources to ensure the integrity of the virtual resource and to monitor the change of virtual resource. Separate authorizations and procedures for users or terminals accessing cloud computing services are required, and risk management for cloud computing is required by establishing appropriate control policies and security technology plans. The detailed contents can be referenced in our previous paper [1].
# ICN-Oriented Mobility Support Method for Dynamic Allocation of Mobile Data Flows
## 3 How Can Dynamic Allocation Flows Be Realized in Mobility Support?  
In Section 3.1, we first introduce an overview of mobility support architecture in ICN. We describe details of the proposed dynamic allocation of mobile data flows in Section 3.2. In Section 3.3, we model the mobile data flow assignment problem as a late-binding node selection problem and build a model to solve this problem using minimized average packet delay as the optimization target; thus proving that the late-binding node selection problem is a special MMKP that is NP-hard.
# Bluetooth Communication Leveraging Ultra-Low Power Radio Design
## 3 BLE Radio Design Trends and Considerations
### Transmitter Design Trends  
A generic architecture of a BLE transmitter is shown in Figure 8, summarizing the main blocks referred to in this section. The following design trends are observed in recent BLE transmitter publications.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & **[12]** & **[16]** & **[39]** & **[18]** & **[46]** & **[47]** & **[40]** \\  & **JSSC’21** & **JSSC’19** & **RFIC’18** & **CICC’18** & **ISSCC’20** & **ISSCC’20** & **ISSCC’18** \\ \hline
**Standard** & BLE WuRx & BLE B.C. & BLE B.C. & BLE B.C. & BLE4.0 & BLE4.0/5.0 & BLE4.0/5.0 \\ \hline
**Technology** & 65 nm & 40 nm & 65 nm & 65 nm & 40 nm & 22 nm & 40 nm \\ \hline
**Supply** & \multirow{2}{*}{0.5 V} & \multirow{2}{*}{1.0/0.9 V} & \multirow{2}{*}{1.1/0.9 V} & \multirow{2}{*}{0.75 V} & \multirow{2}{*}{0.9/1.0 V} & \multirow{2}{*}{0.5 V} & \multirow{2}{*}{0.8 V} \\
**Voltage** & & & & & & & \\ \hline \multirow{3}{*}{**Modulation**} & FSK: 3-channel & \multirow{3}{*}{FSK: 3-channel & \multirow{3}{*}{\begin{tabular}{} \end{tabular} } } & \multirow{3}{*}{\begin{tabular}{} \end{tabular} } & \multirow{3}{*}{\
\begin{tabular}{} \end{tabular} } & \multirow{3}{*}{
\begin{tabular}{} \end{tabular} } \\  & voting + pkt & & & & & \\  & length + pkt & & & & & \\  & interval + RSSI & & & & & & \\ \hline
**Data Rate** & N/A & 250 kbps & 112.5 kbps & 333 kbps & 1 Mbps & 1/2 Mbps & 1/2 Mbps \\ \hline \multirow{3}{*}{**Latency**} & 1 adv. packet & 1 adv. event & 1 adv. packet & \multirow{2}{*}{N/A} & \multirow{2}{*}{N/A} & \multirow{2}{*}{N/A} & \multirow{2}{*}{N/A} \\  & \(\sim\)200 \(\mu\)s & & 1.47 ms & \(\sim\)200 \(\mu\)s & & \\ \hline
**RX** & \multirow{2}{*}{\(-85\) dBm} & \multirow{2}{*}{\(-82.2\) dBm} & \multirow{2}{*}{\(-57.5\) dBm} & \multirow{2}{*}{\(-76.6\) dBm} & \multirow{2}{*}{\(-86\) dBm} & \multirow{2}{*}{\(-96.4\) dBm} & \multirow{2}{*}{\(-95\) dBm} \\
**Sensitivity** & & & & & & \\ \hline
**Power Consumption** & \multirow{2}{*}{220 \(\mu\)W} & \multirow{2}{*}{1200 \(\mu\)W} & \multirow{2}{*}{150 \(\mu\)W} & \multirow{2}{*}{230 \(\mu\)W} & \multirow{2}{*}{2100 \(\mu\)W} & \multirow{2}{*}{1900 \(\mu\)W} & \multirow{2}{*}{3040 \(\mu\)W} \\
**SIR [dB]** & & & & & & \\
**@2 MHz** & & & & & & \\ \hline
**Die Area** & 2.4 mm\({}^{2}\) & 1 mm\({}^{2}\) & 1.1 mm\({}^{2}\) & 4 mm\({}^{2}\) & 1.3 mm\({}^{2}\) & 1.9 mm\({}^{2}\) & 0.8 mm\({}^{2}\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison table for the state-of-the-art Bluetooth-Low Energy receivers.  
Figure 8: Conventional architecture of BLE transmitters.
# PCB Network Analysis for Circuit Partitioning
## 4 Circuit Partitioning Based on Net Attributes and Patterns
### Scattered Components Description  
Unfortunately, some components that are between important networks will be scattered without affiliation to any subset of the IC chip. As shown in Figure 5, scattered components, for example, capacitors C455 and C456, are located between the power supply network \(S\_IMGD+3.3V\) and GND. When the important networks are cut, C455 and C456 are scattered because they could belong to any module, similar to C456, C447, and C430. The problem is that we do not know the affiliation for these capacitors just from the netlist; however, some clues are found from the schematic diagram of circuits. Let \(\mathcal{N}_{S}\), \(\mathcal{N}_{P}\), \(\mathcal{N}_{G}\) represent the sets of signal net, power supply net, and GND net, respectively. We summarize the scattered components into the following categories:  
1. Components scattered between signal network \(\mathcal{N}_{S}\) and signal network \(\mathcal{N}_{S}\), denoted by \(\textbf{X}_{SS}\).
2. Components scattered between signal network \(\mathcal{N}_{S}\) and power supply network \(\mathcal{N}_{P}\) denoted by \(\textbf{X}_{SP}\).
3. Components scattered between signal network \(\mathcal{N}_{S}\) and GND ground network \(\mathcal{N}_{G}\), denoted by \(\textbf{X}_{SG}\).
4. Components scattered between power network \(\mathcal{N}_{P}\) and GND ground network \(\mathcal{N}_{G}\), denoted by \(\textbf{X}_{PG}\).  
In actuality, the cases would be more complicated, because \(\textbf{X}_{SS}\), \(\textbf{X}_{SP}\), \(\textbf{X}_{SG}\), and \(\textbf{X}_{PG}\) could have intersection. Here, we just simplify the description. Scattered components in the netlists mainly include capacitors, resistors, inductors, and others. The numbers of capacitors and resistors are the most. Capacitors between the power network and GND network mainly play four roles: filtering, voltage regulation, energy storage, and bypass energy storage, while resistors mainly play the role of bleeder resistance effect. Assuming that the scattered components between the \(m\)th net and the \(n\)th net are adjacent, denoted  
Figure 4: Schematic diagram for initial partitioning by cutting important attribute networks, better viewed in color.  
by \(\{x_{1}^{mn},x_{2}^{mn},\ldots,x_{N}^{mn}\}\in\mathbf{\chi}^{mn}\), we introduce three rules to perform assignment of these scattered components to modules.
## 4 Discussion  
### The Mechanism of Pore Structure Differences  
#### 4.1.1 The Evolution Stage of Organic Matter  
Shale pore structure parameters in our study mainly include four parameters: pore volume, porosity, specific surface area, and pore size distribution. There have been many studies on the pore structure of marine shale [20; 40; 41]. Previous studies attribute the high micropore volume and specific surface area to the high TOC content [42; 43]. Although the positive relationship has also been discovered in our research of marine shale (Figure 7a), micropore volume and specific surface area are not significantly related to TOC in continental shale (Figure 7a). Comparing marine shale and continental shale showed that TOC content is not the only control factor for the pore structure difference. Instead, another possible reason for the difference is the evolution stage of organic matter. The thermal evolution degree of continental shale is generally low (Ro: 0.7-1.1%) [4; 39]. Many liquid hydrocarbons were generated within the oil generation window, filling the original inorganic pores. The continuous compaction during the diagenesis process makes the original pore space gradually squeeze and disappear, further reducing the overall pore volume. It also leads to the fact that although continental shale has a high TOC, it contributes little to the overall pore volume and specific surface area (Figure 6). However, with the continuous evolution of organic matter in higher formation temperature, the organic matter is in the gas window, and the solid bitumen is gradually cracked. Finally, with increasing thermal maturity, the organic matter in marine shale is in the over-mature stage (Ro: 2.1-2.8%), and many gas hydrocarbons will be generated [22; 44], resulting in abundant micropores and mesopores [40]; the above evolution process also can be testified by the Pyrolysis experiment, the newly generated micropore, and mesopore volume show a positive relationship with temperature [45]. Moreover, due to the effect of thermal maturity difference on micropores, the contribution rate of the organic micropores of marine shale to overall pores is 1.7 times that of continental shale. On the other hand, there is a weak correlation between macropore volume and specific surface area and TOC content (Figure 7c,f), which is related to the organic matter pores mainly consisting of micropore and mesopore [46].  
Figure 7: The correlation between the pore volume (**a–c**) of different pore sizes and TOC. The correlation between the specific surface area (**d–f**) of different pore sizes and TOC. Part of data from ([PERSON] et al., 2020) [20].
## 3 Results and Discussion
### Optimization Design of the Bag Filter  
#### 3.2.1 Structural Optimization Design  
The numerical simulation results for the multi-chamber bag filter model indicate that the flow field distribution near the chamber walls, away from the hopper inlet, is less uniform in each chamber, with a higher overall flow velocity. As a result, the filter bags in this region experience more severe wear. To improve the airflow distribution before entering the filter chamber, this study proposes an optimization measure that incorporates three vertical descending deflectors within the hopper.  
Initially, three sets of models were developed, each featuring different deflector lengths with fixed spacing between the deflectors (Schemes 1-3). Additionally, three other sets of models were created with fixed deflector lengths and varying spacing between the deflectors (Schemes 1, 4, 5). Subsequent optimization results will guide the adjustment of combinations of different deflector lengths and spacings. The intake channel of the hopper has a diameter of 500 mm, and the 1304 mm long deflector is aligned with the center axis of the inlet channel at its bottom end. Detailed information about each optimization model with added deflectors is provided in Table 2. A schematic representation of Three Deflectors Scheme 1 is shown in Figure 9.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Scheme** & **Length of the Deflectors (mm)** & **Spacing Between the Deflectors (mm)** \\ \hline Three Deflectors Scheme 1 & 1054-1304-1554 & 965-965-965-965 \\ Three Deflectors Scheme 2 & 804-1054-1304 & 965-965-965-965 \\ Three Deflectors Scheme 3 & 1304-1554-1804 & 965-965-965-965 \\ Three Deflectors Scheme 4 & 1054-1304-1554 & 905-945-985-1025 \\ Three Deflectors Scheme 5 & 1054-1304-1554 & 1130-800-800-1130 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Optimized model details for adding deflectors (full-scale).  
Figure 9: Schematic diagram of Three Deflectors Scheme 1.
## 4 Discussion
### Limitations and Future Work  
Based on the assumptions made, the present simulation only considers the impact of mechanical factors and does not account for the influence of groundwater. The inclusion of groundwater effects would provide a more accurate estimate of settlement results. Additionally, due to the limitations of computing resources, a plane strain model was employed for the numerical simulation calculation model instead of a three-dimensional one. With improved computational efficiency, a three-dimensional calculation model could be developed to study the distribution of cracks in the tunnel's three-dimensional space, allowing for more precise identification of the areas where cracks may arise.
# Design of a Chaotic Trajectory Generator Algorithm for Mobile Robots
## 7 Experimental Results
### Coverage of the Work Area  
In order to estimate the work area covered by the chaotic trajectories imposed on the mobile robot, it was divided into a grid of 12 \(\times\) 20 (240) frames with lateral measurements of 76.66 mm, so we can have a more precise idea of the effective coverage of the proposed Algorithm 1 for exploration or search applications. In Figure 18 the results obtained from the trajectory of the Khepera III robot with initial conditions \(x_{0}=1.1\), \(y_{0}=-0.9\), \(\theta=1.8^{\circ}\) and \(x_{0}=232.1\), \(y=685.3\), \(\theta=-10.3^{\circ}\) are shown.  
Figure 18: Experimental work area coverage for initial conditions: (**a**) \(x_{0}=1.1\), \(y_{0}=-0.9\), \(\theta_{0}=1.8^{\circ}\); (**b**) \(x_{0}=232.1\), \(y_{0}=685.3\), \(\theta_{0}=-10.3^{\circ}\).  
We can see that, with only 200 iterations of the chaotic trajectory generator algorithm, the test area is covered almost entirely. It should be remembered that the area occupied by the robot and the range of its sensors are not taken into account.
# Efficient Control of a Non-Linear System Using a Modified Sliding Mode Control
## 3 Simulation and Experimental Results
### Simulation Results of SMCSPO and PDSMCSPO
#### 3.2.2 PDSmcspo  
The control input to the system in the case of PDSMCSPO is shown below.  
When \(|\hat{s}|>\varepsilon_{c}\)  
\[\overline{u}=\frac{1}{\alpha_{3}}[-\frac{K^{\prime}}{k_{d}}sat(\hat{s})+ \tilde{x}_{1}\left\{\frac{k_{p}\cdot k_{1}}{k_{d}\cdot\varepsilon_{o}}+\frac{ k_{2}}{\varepsilon_{o}}-\frac{k_{1}^{2}}{\varepsilon_{o}}\right\}-\frac{k_{p}}{k_{d}} (\hat{x}_{2}-\dot{x}_{d})-\hat{\psi}+\ddot{x}_{d}] \tag{63}\]  
Figure 11: Effect of disturbance on sliding surface.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Serial. No** & **SMC** & **PDSMC** & **Disturbance** \\ \hline
1 & affected & affected & D \textless{} 275 (rad/s) \\
2 & affected & not affected & 275 \textless{} D \textless{}550 \\
3 & not affected & not affected & D \textgreater{} 550 \\ \hline \end{tabular}
\end{table}
Table 3: Effect of different frequency disturbances in sliding surface.  
When \(|\hat{s}|\leq\varepsilon_{c}\)  
\[\overline{u}=\frac{1}{\alpha_{3}}[-\frac{K^{\prime}\cdot\hat{s}}{k_{d}\cdot \varepsilon_{c}}+\widetilde{x}_{1}\left\{\frac{k_{p}\cdot k_{1}}{k_{d}\cdot \varepsilon_{o}}+\frac{k_{2}}{\varepsilon_{o}}-\frac{k_{1}^{2}}{\varepsilon_{o }}\right\}-\frac{k_{p}}{k_{d}}\big{(}\hat{x}_{2}-\hat{x}_{d}\big{)}-\hat{\psi} +\widetilde{x}_{d}] \tag{64}\]  
System parameters for both schemes are shown in Table 4.
# DMFO-CD: A Discrete Meth-Flame Optimization Algorithm for Community Detection
## 5 Experimental Evaluation
### Convergence Evaluation  
In this subsection, the DMFO-CD convergence behavior and speed are assessed and compared to the comparative algorithms except GA-net because it uses community score to calculate the fitness. Figure 12 shows the convergence curves of all algorithms on eleven datasets. These curves show the best modularity in every iteration for each algorithm over 30 runs on karate, dolphins, football, polbooks, four webkb datasets, and adjnoun networks and 10 runs on email-Eu and dblp networks. The convergence curves show that DMFO-CD on karate, dolphins, football, and polbooks datasets is better than GACD and EGACD, and is competitive with DPSO-PDM and DECS. In webkb datasets, the convergence curve of DMFO-CD follows closely GACD and EGACD, and in adjnoun dataset, DMFO-CD has the best convergence curve versus other comparative algorithms. In email-Eu dataset, DMFO-CD does not have a good convergence, but in dblp, it has the best performance after DPSO-PDM. The better convergence of DMFO-CD originates from the usage of the best neighbor in the initialization step. The convergence of the DMFO-CD is sped up when the best neighbor is used in the initialization step versus when the best neighbor is not used. Figure 13 shows this difference on four selected datasets.
# Bi-Directional Point Flow Estimation with Multi-Scale Attention for Deformable Lung CT Registration
## 3 The Proposed Approach  
In this section, we first formalize our bi-directional objective for deformable lung CT registration (Section 3.1), describe the multi-scale point feature extraction backbone (Section 3.2), elaborate on geometric keypoint attention (Section 3.3), detail the progressive refinement of the flow predictions with flow attention (Section 3.4), and finally outline our comprehensive bi-directional training objective (Section 3.5).
## 4 Discussion
### Evaluation on Whole Edge-Cloud System  
Table 9 shows the experiment results to verify the effectiveness of the cloud-based detection results verification mechanism. In this experiment, the Edge-YOLO-CA with acceleration strategy generates detection results of surveillance video on edge devices, while the YOLOv5s deployed on cloud devices verifies and corrects the detection results due to the cloud-based detection results verification mechanism mentioned in Section 3.3. As shown in Table 9, with only checking less than one-tenth of frames, the edge detection results can be corrected to close to the cloud detection results.
# Monitoring a Pre-Normative Multi-Family Housing Case-Study in a Mediterranean Climate
## 4 Results
### Indoor Temperature
It should hence be stated that this thermal homogeneity has been obtained in free evolution thermal conditions, that is without any kind of cooling devices in the bedroom and with minimum use of those in the living-room. In January, indoor temperature variations reflect the evolution of outdoor temperatures. Thus, the day on which outdoor temperature values reached the lowest value (20 January; T\({}_{\text{min}}\) 6.3 \({}^{\circ}\)C, at 21:00), indoor thermal oscillations went up by up to 2.5 \({}^{\circ}\)C in both rooms, with an indoor temperature range between 16 \({}^{\circ}\)C and 18 \({}^{\circ}\)C, that is within the comfort band limits. Due to the lack of centralized heating, the user has had to sporadically turn on electric radiators on the coldest days of January. This conclusion can be drawn from the sharp changes in the registered indoor temperature values, mainly on 24 and 25 January, and from 28-30 of the same month (Figure 9c). The data record for April show that, for a large number of hours, indoor temperature values in the monitored rooms have remained very near the comfort band limits (21.3 \({}^{\circ}\)C-26.3 \({}^{\circ}\)C), according to those obtained by ASHRAE's adaptive comfort method [(34)] (Figure 9a). From April onwards and during the whole summer season, indoor temperature values rise significantly and even reach 30 \({}^{\circ}\)C, although they remain within the comfort range for most of the day, in accordance with the limits obtained for the summer season and specifically the month of July in this case (23.9 \({}^{\circ}\)C-28.9 \({}^{\circ}\)C) [(34)] (Figure 9b). However, the data record for January show that indoor temperature values have remained below winter comfort limits (19.6 \({}^{\circ}\)C-24.6 \({}^{\circ}\)C) [(34)] most of the time, with temperature values that provoke uncomfortable conditions for the user (Figure 9c). This inability to preserve adequate comfort conditions, mainly due to the lack of basic services that would help the housing unit fight against indoor thermal loads, leads to the \"energy poverty\" concept, which remains a major issue to be solved by the energy retrofitting of residential buildings in the Mediterranean area [(9; 10; 34)].
## 4 Results and Discussion
### Experimental Setups  
To verify the effectiveness of HTMC-PGT, this study uses a 7:3 ratio to partition the dataset, forming a training set (70%) and a validation set (30%), with the testing set being the same as the validation set. We do not plan to optimize the hyperparameters (mentioned later) in the experiments, aiming to simplify constructing the classifier tree. Therefore, preserving an independent testing set is unnecessary, which can also avoid inconsistent data distribution between datasets (for training, validation, and testing) when randomly extracted from the original dataset.  
Document Model Settings. We set the word count of PGT documents to 600, which means that documents exceeding this limit will be truncated, whereas documents below this length will be filled with the placeholder \"[PAD]\". Each document is divided into 150 segments, each containing four words.  
XLNet Settings. We downloaded the pretrained model \"chinese-xlnet-base\" from Hugface, which has 13 hidden layers, each with a dimension of 768 [18]. Because the last hidden layer extracts the semantic information of words in the original text, we use the output of this layer as the char-level embeddings to input the classification model of PGT [35].  
Training Details [28; 38]. We use Adam as the optimizer and set the learning rate to 0.0005, the weight rate to 0.0001, and the batch size to 32. In addition, for the sub classifier \(i\) whose parent node \(j\) in the classifier tree Y has already obtained the optimal training parameters, the parameters' values \(\xi_{j}\) of HAs in the parent node classifier \(j\) can be directly used as the initial value of the corresponding parameters \(\xi_{i}\) of the sub classifier \(i\). Allparameters in HAs and FC can be automatically tuned during training. At the same time, to avoid overfitting, we use dropout technology [39] and set the dropout rate to 0.5.  
Platform Settings. These experiments are implemented on the Windows Server 2020 platform with Intel Core processors [EMAIL_ADDRESS] GHz and a memory of 32 GB (DDR4 2666 MHz 16 GB * 2). The program is encoded in Python 3.6 and uses Pytorch 1.7.0 as its machine learning library. To improve the efficiency of the tensor calculation, the machine is equipped with a graphics card (NVIDIA GeForce GTX 1080 Ti) with a graphics memory of 11 GB, and the CUDA (Compute Unified Device Architecture) version is 10.2.