esa-sceva/satcom-chunk-collection huggingface dataset:
# An Intelligent Approach to Allocating Resources within an Agent-Based Cloud Computing Platform
## 4 Intelligent Model for Allocating Resources
### Compaction  
The proposed model uses the key characteristics of virtualization technology, which involves migration of machines between physical servers. In fact, this characteristic is very effective when compacting VMs into the smallest possible number of servers. This allows one to turn off/switch to sleep mode the set of physical servers that have not yet been assigned a machine, which significantly increases energy efficiency.  
However, problems occur when the machines are widely dispersed, which can happen when a \(SSA\) associated with a specific service detects various nodes that are at the highest priority level (they have a high quality of results) and requests the random elimination of one of these nodes.  
The compaction process is simple and is applied by the specialized \(GMA\) as follows:  
1. A virtual server with a very low number of VMs (the number is determined by the system administrator) asks other servers to host its VMs so that it can go into sleep mode and not use any resources.
2. The nodes with available resources at the time of the request evaluate the snapshot (\(l^{t}_{PR_{i}}\)) provided by the \(LMA\) to determine whether it can host another machine with the given characteristics.
3. If there are resources available, the configuration is sent to the \(GMA\) that has made the request, and the migration process is initiated. If this agent receives various confirmations simultaneously, it randomly initiates the migration process with one of the machines. The process is random since the agent does not know all the internal details of the machine that hosts the new virtual node.  
This simple process makes it possible to compact the set of VMs without affecting the quality of service, since the individual resources of each machine are not modified. The system then goes into a compact state.
## 3 Signal Processing
### Feature Extraction Methods  
The second component is feature extraction. Once you have selected the correct control signal, it is necessary to find a way to better represent it. BCI mainly use four kinds of feature extraction methods to represent the signal: temporal methods (i.e., signal amplitude and auto-regressive), frequency methods (i.e., band power and power spectral densities), time-frequency methods (STFT and wavelets) and some others (e.g., coherency, phase synchronization, etc.). The selection of one of these methods will depend completely on the desired control command for classification. Thus, depending on the transformation, it is recommended that EEG recordings have a high sampling rate and more than a single electrode for a better signal recording.
## 5 Parameters Study
### Influence of Interference Force  
During vehicle operation, various factors, such as random vibrations of the vehicle body, can introduce interference forces. Although these forces are typically small, they can indeed exert an influence on frequency identification. To account for this effect, Gaussian white noise interference forces with specific power levels were considered. For the vehicle parameters mentioned before, including a speed of 10 m/s, weight of 350,000 kg, and stiffness of 11,667 kN/m, white noise interference forces with power levels of 20 dBW, 30 dBW, 40 dBW, and 50 dBW were applied. The corresponding standard deviations of these interference forces were 10 N, 31.6 N, 100 N, and 316.2 N, respectively.  
Figure 25: Acceleration and frequency responses of the test vehicle at different vehicle masses: (a) \(m_{v}\) = 1200 kg; (b) \(m_{v}\) = 5000 kg; (c) \(m_{v}\) = 15,000 kg; (d) \(m_{v}\) = 25,000 kg.  
The acceleration response and spectrum of the vehicles under different power levels are shown in Figure 26. As the power of the interference force increased, the amplitude of the interference frequency on the spectrum diagram gradually increased. When the power of the interference force reached 40 dBW (with a standard deviation of the interference force to vehicle gravity ratio at 0.029%), the influence of the interference force was relatively small. Beyond 40 dBW, the influence of the interference force became more significant, and only the fundamental frequency could be distinguished at 50 dBW.  
Figure 26: Cont.
## 3 Numerical Results
### Analysis of Heat Release Rate (HRR) in the Fire Compartment  
The evolution of the heat release rate (HRR) reflects the sensitivity of the combustion process to ventilation conditions and to dynamic events that alter the oxygen balance within the compartment. All three scenarios (Figure 20) exhibit a similar initial increase in HRR, up to the point of reaching a fully developed fire, around t = 120-150 s, after which their behavior diverges.  
Figure 19: Variation in CO\({}_{2}\) concentrations at point P2 in the stairwell for scenarios S0, S1, and S2.  
Figure 20: HRR variation across the three scenarios.  
* In scenario S0, the HRR reaches approximately 6000 kW and stabilizes in an oxygen-limited burning regime, with minor fluctuations governed by thermal buoyancy and passive air leakage.
* In scenario S1, the HRR is significantly influenced by the moment the door is opened, allowing air from the stairwell to enter the compartment. Although the system mitigates abrupt variations, this air inflow triggers a transient increase in HRR, confirming that pressurization alone cannot fully prevent combustion intensification when a direct opening is created.
* In scenario S2, the HRR exhibits the highest instability, with frequent peaks exceeding 7000 kW. This behavior is explained by the combined effect of the early door opening and the placement of the lower fan on the ground floor, in close proximity to the fire source. The fresh air introduced from below flows directly into the combustion zone, fueling the fire in an uncontrolled manner and promoting oscillatory burning regimes that are difficult to stabilize.  
Scenario S2 generates the most unstable combustion regime due to bidirectional ventilation and the placement of the lower fan near the fire source. Scenario S1, although temporarily affected by the door opening, provides better control over HRR. Scenario S0 exhibits stable but oxygen-limited combustion. Optimizing ventilation involves not only selecting the appropriate airflow rate but also strategically positioning air inlets to avoid directly feeding the fire source.
## 6 Conclusions  
We studied inter-cluster cooperative reception for the uplink of a two-cluster C-RAN system, where the BBUs of two neighboring clusters communicate with each other via finite-capacity backhaul links with the goal of mitigating the impact of inter-cluster interference signals. To overcome the limitation of conventional cooperation scheme, in which each RU produces a single description, we proposed an improved cooperation strategy, whereby each RU performs layered compression to produce two descriptions, among which only a single description is forwarded to the neighboring BBU and the compression rate allocation is subject to optimization. We tackled the optimization of the proposed cooperation scheme with the goal of maximizing the sum-rate of all the users. Via numerical results, the advantages of the proposed cooperation scheme compared to the baseline schemes with no or conventional inter-cluster cooperation were validated. As future work, we mention the analysis of the proposed cooperation scheme while taking into account the system complexity of successive refinement quantization strategy and the robust design of inter-cluster cooperation strategies in the presence of random packet losses on the fronthaul and backhaul links.  
Conceptualization, J.K. and S.-H.P.; Methodology, J.K. and S.H.P.; Software, J.K. and S.H.P.; Validation, J.K.; Formal Analysis, J.K. and S.-H.P.; Investigation, J.K.; Resources, J.K. and S.-H.P.; Writing--Original Draft Preparation, J.K.; Writing--Review & Editing, S.-H.P.; Visualization, J.K.; Supervision, S.-H.P.; Project Administration, S.-H.P.; Funding Acquisition, S.-H.P. All authors have read and agreed to the published version of the manuscript. This work was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) grants funded by the Ministry of Education under Grants NRF-2018R1D1A1B07040322 and NRF-2019R1A6A1A09031717. The authors declare no conflict of interest.
### Advantages and Disadvantages of NCS  
There are several underlying advantages of an NCS, which is termed as the next generation control system. Key merits of networked control systems (NCSs) are as follows:  
1. _Effective reduction of system complexity_: It is possible to minimize the complexity of control systems by interfacing them with the communication network. With the communication network, the data related to multiple plants can be easily stored at a single server which reduces the complexity of control systems.
2. _Efficient sharing of network data_: Several parameters pertaining to the plant can be exchanged easily using the communication networks, which helps in the design of control algorithms.
3. _Simple to take intellectual decisions based on the information_: The information can be used to make intelligent decisions easily.
4. _Eliminates unnecessary wiring_: Today, it is possible to transmit data wirelessly at a very high speed. The wiring for interfacing controllers and plants can also be avoided. The wireless sensor networks are advanced enough to make the wireless control is a reality.
5. _Simple to scale the networks by adding additional sensors, actuators, and controller_: The wireless sensors and actuators can be replaced easily, which reduces the maintenance cost of the NCS. The controllers can also be replaced economically as compared to the traditional wired controllers. The expansion of sensors, actuators and controllers can also be achieved easily.
6. _Cyber-physical interface for tele-execution of control_: The NCS provides a platform for cyber-physical interface and tele-execution of controls. The NCS permits the remote control of the plant.  
Figure 1: A simple block representation of a networked control system (NCS).  
* _Wide range of applications_: Applications in the area of distributed power systems, robots, unmanned aerial vehicles (UAVs), automobiles, space discovery, terrestrial discovery, industrial unit automation, remote problem-solving and troubleshooting, perilous environments, aircraft, production plant monitoring, and many more. More and more applications of NCS are coming into existence every day. The NCS is also plagued by several problems. The disadvantages of NCS are: 1. _Loss of ability to determine the time of incoming data_: The time at which the data arrives cannot be determined exactly, so uniform sampling cannot be used. 2. _Loss of data integrity:_ Data may be lost during the transmission process, so it cannot give the complete information. 3. _Communication Latency:_ In remotely located NCSs, due to the communication latency, the control action may not be implemented with immediate effect. 4. _Complexity and Congestion:_ As the number of nodes increases, so does the complexity of the communication system, causing congestion and time delays. Therefore, the deployment in an industry with tens of thousands of sensors and actuators can be a challenging task.
## 2 Materials and Methods
### Linear Arch Static Analysis (LASA)
#### 2.1.1 Geometry  
In Figure 2 the 1D space Linear Arch \(\Gamma^{*}\) and its plane projection \(\Gamma\) onto the plan-form \(\Omega\) are depicted. Both \(\Gamma\) and \(\Gamma^{*}\) are parametrized with the arc length \(s\) along \(\Gamma\). Referring to the same figure for the notation, we have:  
\[\Gamma=\{\mathbf{x}=\mathbf{x}(s),\;s\in[0,L]\}, \tag{1}\]  
\[\Gamma^{*}=\{\mathbf{x}^{*}=\mathbf{x}(s)+f(s)\mathbf{\hat{e}}_{3},\;s\in[0,L]\}, \tag{2}\]  
\(L\) being the length of the curve \(\Gamma\), and the elevation \(f(s)\) being a continuous function.  
The tangent and normal unit vectors to the plane curve \(\Gamma\) can be defines as:  
\[\mathbf{\hat{t}}=\frac{d\mathbf{x}}{ds},\quad\mathbf{\hat{n}}=\mathbf{\hat{e} }_{3}\times\mathbf{\hat{t}}, \tag{3}\]  
and, the unit tangent vector along \(\Gamma^{*}\) as:  
\[\mathbf{\hat{t}}^{*}=\cos\varphi\;\mathbf{\hat{t}}+\sin\varphi\;\mathbf{\hat{ e}}_{3}. \tag{4}\]  
Finally,  
\[\frac{d\mathbf{\hat{t}}}{ds}=\rho\mathbf{\hat{n}}, \tag{5}\]  
where \(\rho\) is the curvature of \(\Gamma\).
## 2 Materials and Methods
### Grinding Test
Meanwhile, the grinding force and grinding temperature were synchronously recorded by mechanical sensors and infrared thermometers during the test. Before the test, anhydrous ethanol was used to clean the surface of the rail sample and grinding wheels to ensure that the influence of impurities was eliminated. The rail samples and the grinding wheels were weighed before and after the grinding test. The mass loss of grinding per unit time for the rail sample was defined as the material removal ratio of rail or grinding efficiency, and the ratio of grinding mass loss between rail and grinding wheel was defined as the grinding ratio. The information of the main devices used to construct the grinding mechanism is as follows: tool grinder (BDM-901C, DiMontega, Beijing, China), force sensor (VC121D-50, Viste, Shenzhen, China) and infrared thermometer (TA8605, TASI, Suzhou, China). The signals acquired from the test were transformed into digital signals through a converter and stored in computer through a Labview program. The surface topography was observed by Ultra-fine digital microscope (VHX-7000, KEYENCE, Osaka, Japan). The surface roughness of rail before and after grinding test was measured and recorded by 3D optical surface profilometer (Ze Gage TM, Zygo, Middletown, CT, USA). The surface residual stress was inspected via the X-ray (Proto iXRD, LaSalle, ON, Canada). In addition, the grinding debris was observed via SEM and analyzed by EDS (S-3400 N, Hitachi, Tokyo, Japan) and XRD (D/MAX-2500, Rigaku, Shishima, Japan).
## 3 Problem Formulations
### Sensing-Based Spectrum Sharing under Imperfect Sensing Conditions
It is seen that ergodic capacity trends are consistent  
Figure 4: Ergodic capacity versus \(I_{av}\) for different \(P(\mathcal{H}_{i})\) with \(P_{av}=15\,\mathrm{dB}\) and \(m_{sp}=1\). with the findings in Figure 4. Since the smaller values of \(m_{sp}\) correspond to the more severe fading condition of the interference link, which is a benefit for the satellite user link, the ergodic capacity is inversely proportional to \(m_{sp}\). The result is consistent with the results obtained in Equations (7) and (27).
# An Image Authentication Scheme Using Merkle Tree Mechanisms
## 3 Proposed Method
### Merkle Tree Generation  
The first part in the generation of the Merkle tree process is as follows, the image is sliced into several blocks, then encrypted and uploaded to the IPFS system. Before generating the Merkle tree, we have to do some image processing. First, we take \(k\) MSBs (Most Significant Bit) of each pixel from the original image \(l_{org}\) as the new image \(l^{{}^{\prime}}\). Next, the image is sliced \(l^{{}^{\prime}}\) into non-overlapping blocks \(B_{i}\), where the block size is \(N\times N\) pixels. After that, each block is encrypted \(B_{i}\) and uploaded to the IPFS system. As the IPFS is a decentralized file system that anyone who knows the file address can  
Figure 5: The verification of Merkle tree.  
browse to to access the files, for file protecting purposes, any encrypting system can be adopted to our method, such as XOR Cipher, RSA, AES, etc. The process is shown in Figure 6.  
When the file is uploaded to IPFS, the system will apply a hash function to generate the hash value of the image file (also known as the fingerprint to identify the file). Then, the fingerprint is used as transactions in the blockchain \(TX_{i}\) (see Figure 7), and each \(TX_{i}\) as the leaves, to build up the Merkle tree \(M\) (see Section 2.3 ). Meanwhile, the Merkle Root is stored into the blockchain (see Figure 8). Here, using the fingerprint as the transaction method has two advantages. First, the cipher image block is placed in a decentralized system to maintain its availability and remove the redundant files in the whole network. Second, the fingerprint is stored in the blockchain, IPFS can use the fingerprint of the file to locate its address, so that image recovery can also be performed. The key steps of the proposed Merkle tree generation method are summarized as Procedure 1.  
Figure 8: The process of storing data in the blockchain.  
Figure 6: Process of image slicing and encryption.  
Figure 7: Inter-Planetary File System (IPFS) fingerprint as the transactions in the blockchain (leaves of Merkle tree).  
```
0: Original Image \(I_{org}\)
0: Merkle tree \(M\)
1: Take \(k\) MSBs from each pixel in original image \(I_{org}\) as a New Image \(I^{{}^{\prime}}\).
2: Slice Image \(I^{{}^{\prime}}\) into non-overlapping block \(B_{i}\), where the block size is \(N\times N\) pixels.
3: Encrypt \(B_{i}\) by \(B_{i}{}^{e}=E_{K}(B_{i})\).
4: Upload \(B_{i}{}^{e}\) to IPFS and take the unique hash (fingerprint) as \(TX_{i}\), generated by IPFS.
5: Generate Merkle tree \(M\) by taking \(TX_{i}\) as the leaf node of Merkle tree.
6: Store \(TX_{i}\) and the Merkle Root of \(M\) in the blockchain.
```  
**Algorithm 1** Merkle Tree Generation
# Thermal Buckling and Vibration Analysis of SMA Hybrid Composite Sandwich Beams
## 4 Results and Discussion
### Thermal Buckling Analysis
According to these plots, the first dimensionless frequency with TID properties is higher than that one for TD properties, whereas an increased value of \(V_{SMA}\) yields a reduced value of the first frequency for lower temperatures, and an overall increase for temperatures higher than 100 \({}^{\circ}\)C.
## 2 Materials and Methods  
### Sampling  
Water samples from the pelagic zone of Lake Baikal were collected by using an SBE-32 cassette sampler (Carousel Water Sampler, Sea-Bird Electronics, Bellevue, WA, USA) at 21 stations (Figure 1) in May 2015, May and September 2016, May 2018, May and September 2019, and June and September 2020 and 2021 (Table S1, supplementary materials). At each station, two samples were taken in 1 L glass bottles, to which 0.5 mL of a 1 M aqueous solution of sodium azide (Merck, Darmstadt, Germany) was added as a preserving agent. Water bottles were closed using a lid with an aluminium foil gasket and stored at 5 \({}^{\circ}\)C until laboratory analysis.  
Baikal omul, _Coregonus migratorius_, [PERSON], 1775 (_C. migratorius_), is fish species meeting the criteria for biological objects to be selected as bioindicators [16]: (i) _C. migratorius_ migrates throughout the entire water area of Lake Baikal in a depth range up to 350 m, including the zones of river runoff; (ii) it is a commercial fish species, ensuring economic efficiency of the sampling stage during monitoring; (iii) individuals at the age of three to seven years accumulate PCBs in an amount sufficient for their measurement with the  
Figure 1: Map of Lake Baikal and sampling sites. At stations 1, 2, 4, 5, 7, 10, 12, 17, 18, 20, and 21, sampling was carried out from the upper water layer (5 m) at a distance of up to 3 km from the coast at station 3, 6, 8, 11, 16, and 19 at reference central stations; at station 6—from the water column at depths of 5 to 1200 m; at station 11—from the water column at depths of 5 to 800 m; and at station 22, _C. migratorius_ was caught.  
required accuracy [53; 54]. _Coregonus migratorius_ was caught near station 22 (Figure 1) in March to July 2014, February 2016, March 2018-2020, and September 2017-2018. Collected fish were packed in aluminium foil, frozen, and stored at a temperature of \(\leq\)minus 18-20 \({}^{\circ}\)C. Identification characteristics of the selected Baikal omul (sex and age, Table S2) were determined according to the methods in [55; 56]. The group of fish selected as bioindicators of PCBs included _C. migratorius_ aged from three to seven years. The choice of this species of Baikal fish as bioindicators ensured the efficiency of the sampling because Baikal omul belongs to a commercial species.  
The following reasons determined the choice of sampling seasons for water and _C. migratorius_. In the spring, after the ice melts, PCBs enter the lake water due to the melting of ice on the water surface of the lake and snow on its coast, in which PCBs have accumulated during the winter. Water samples collected in autumn reflect the accumulation of PCBs in water, which have come from the atmosphere in summer.
## 2 Related Work
PCAQR [13] is a new network topology that realizes cross-layer application by optimizing the transmission power and routing path of nodes, reducing energy consumption and delay. CLORP [27] is a cross-layer opportunistic routing protocol that uses multi-agent reinforcement learning with two reward functions for successful and failed transmissions, combined with cross-layer information to optimize routing. It employs an adaptive learning rate and a Q-value initialization strategy based on location and neighbor count to accelerate convergence and adapt to dynamic network topology changes. CLIC utilizes an integrated routing MAC to adaptively avoid nodes likely to experience high conflict or congestion during routing [28]. This approach provides a high PDR and low latency with low overhead. Nevertheless, the protocol does not fully account for the distinctive characteristics of underwater acoustic channels, such as signal propagation loss and multipath effects, which can influence the efficacy of data transmission. GO-MAC [17] employs a novel approach to determining the next hop node, integrating geographic routing protocols and OFDM techniques. This integration enables the simultaneous optimization of communication resources and the selection of next hop nodes through a handshaking mechanism. The degree of node congestion and channel quality are taken into account to optimize the adaptive backoff algorithm in the geographic routing protocol. Nevertheless, the assessment of the channel quality in this protocol is predominantly based on the ray propagation model to predict the receiver SNR, which primarily focuses on the propagation characteristics of the signals and does not fully consider the physical layer effects in the OFDM modulation and demodulation process. In conclusion, when designing cross-layer routing protocols, several factors must be taken into account in order to ensure the efficiency and reliability of the protocols. Firstly, the protocol design should fully consider the acoustic propagation characteristics, particularly in special environments such as underwater acoustic channels, where signal propagation loss and multipath effects significantly impact data transmission performance. Secondly, cross-layer design factors are also critical, including but not limited to congestion state, residual energy, channel quality, and hop count, which collectively determine the merits of routing and the overall performance of the network. Furthermore, it is essential to consider the physical layer characteristics, particularly when employing modulation techniques such as OFDM. The unique physical layer characteristics of OFDM must be fully incorporated into the protocol architecture.