esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 State-of-the-Art
### Access Control Framework  
In the wake of growing demands for decentralized computing due to application latency requirements, privacy, and security, ref. [76] introduced Aerogel, an access control framework designed to bridge the security gap between bare-metal IoT devices and the Wasm execution environment, particularly regarding access control for sensors, actuators, processor energy usage, and memory usage. The framework views the runtime as a multi-tenant environment in which each Wasm-based application is a tenant. It utilizes the intrinsic sandboxing mechanisms of Wasm to enforce access control policies without trusting the bare-metal operating system. Evaluation on a cortexM4 based development board (nRF52840) demonstrated that Aerogel can effectively enforce compute resource and peripheral access control policies with minimal runtime overhead and without additional energy consumption. Another study [77] introduced CAPLets, an authorization mechanism that extends capability-based security to provide fine-grained access control for multi-scale IoT deployments. CAPLets uses a robust cryptographic structure to ensure integrity while maintaining computational efficiency for resource-constrained systems. The system enhances capabilities with dynamic user-defined constraints to describe arbitrary access control policies. The same paper introduced CapVM, a Turing-complete virtual machine, along with eBPF and Wasm, to describe constraints. Empirical evaluation of CAPLets shows that it can express permissions and requirements at a granular level, aiding in constructing complex access control policies. The results demonstrate that CAPLets is significantly faster and more energy-efficient than current IoT authorization systems.
# Involvement of the Orexinergic System in Cancer: Antitumor Strategies and Future Perspectives
## 3 Orexins and Cancer
### Circadian Rhythm and Cancer  
Many of the metabolic processes dysregulated in cancer (inflammation, increased oxidative stress, etc.) are under the regulation of the circadian rhythm. This phenomenon is the basis of the bidirectional relationship that has been found between sleep disorders and certain types of cancer [9]. Furthermore, this influence of the circadian rhythm on cellular processes supports studies describing that the time of day at which anticancer treatment (radio or chemotherapy) is administered is important for optimizing the effects of such treatment, a phenomenon known as chronotherapy [128]. Some studies in animal models have shown that circadian sensitivity to the cellular metabolism of pharmaceutical compounds influences the effect of treatments, which improves their efficacy and minimizes adverse effects if the optimal time for their administration is considered [10]. Although in many cases, a clear relationship between sleep disorders and an increase in the incidence of cancer cannot be assured, the studies carried out so far in humans provide promising results as to the most favorable time of day for the application of chemotherapy [128]. For this purpose, it is essential to know in detail the circadian rhythms of the different cell types. A bidirectional relationship has been found between the incidence of some types of cancer and various sleep disorders (insomnia, hypersomnia, etc.) [9]. The expression of several genes, some of them related to cancer, is also subject to this circadian rhythm, coordinated in the hypothalamus. Orexigenic neurons are located in this brain region and are involved in, among other functions, the regulation of sleep-wake cycles [18]. In fact, it has been proposed that the orexigenic system may be a promising therapeutic target in some types of tumors [9] (see Section 4). Table 1 and Figure 2 summarize the main mechanisms mediated by the orexinergic system in tumor cells.
# A Review of the Impact of Spontaneous Combustion on Slope Stability in Coal Mine Waste Dumps
## 4 Monitoring and Mitigation Strategies Combating Spontaneous Combustion
### Mitigation Strategies
These include multi-phase foams, hydrogel mixtures, and foamed gels; these materials offer potential for fire suppression across various environments. Multi-phase foam and hydrogel mixtures combine the thermal insulation properties of foams with the high water content and cooling capacity of hydrogels, resulting in enhanced fire resistance and heat absorption capabilities. Foamed gels, produced by incorporating gas bubbles into gel matrices, provide prolonged surface adherence and function as both a physical barrier and a heat sink. Another innovative strategy involves integrating foam into traditional grouting materials, thereby improving their fire-suppressive performance while maintaining structural integrity [180; 181; 182; 183; 184; 185; 186]. These materials demonstrate superior cooling and smothering capabilities compared to water or conventional combustion retardants--especially in high-temperature or oxygen-deficient environments. However, they also have limitations. Their production and application tend to be more costly, and they may present environmental and health concerns if not properly managed. Additionally, these materials often require specialized equipment for application, and their long-term effectiveness can be compromised by factors such as evaporation, chemical degradation, or exposure to environmental conditions.
## 4 Results and Evaluation
### Extending Platform Functionality  
The deep knowledge of the underlying hardware and software architecture details favors the implementation of modified node configuration variants to support a wide range of environmental measurements. As an example, the proposed system can be used to provide automated gravimetric soil moisture data harvesting for more accurate evaluation  
Figure 16: Characteristic instances from the equipment being dug up (sensor node #A1 unit, soil sensor instrument, sensor node #A2 battery enclosure) after two months of continuous flawless operation.  
Figure 15: More aggressive LoRa transmitter settings for (**a**) the prototype node (#A2); (**b**) its simulation equivalent using the LoRa Air-Time Calculator tool.  
of the performance of the aforementioned capacitance-based instruments, especially in non-conventional cases, like stone wool subtraces.  
The apparatus used for the specific experiment consisted of a small plastic container, a sample of stone wool, plastic spacers, a digital camera, a precision scale, and the modified data acquisition system. In greater detail, node #A2 was put in the plastic container along with the stone wool and a TEROS 10 sensor connected to it. The stone wool's net weight was initially measured, and to obtain the maximum moisture level, the stone wool sample was entirely immersed in fresh water, while the moisture sensor was placed horizontally inside the left side of the subtrace. The periodic soil moisture measurements arriving from node #A2 (via LoRa) to the RPi gateway triggered the digital camera to take a photo (via a Python language script), which was also stored in a corresponding time-stamped file on the microSD card of the latter gateway.  
The aforementioned component arrangements are shown in Figure 17 (in the left part), along with a typical photo of the scale screen taken automatically (at the top right) and an indicative part corresponding to the sensor node #A2 log file (at the bottom right). Furthermore, as mentioned in Section 3.2, the I2C interface on the 32u4 board was utilized to host an auxiliary Sensirion SHTC3 air temperature and humidity sensor, which is faster, more accurate, and more energy-efficient than its competitors, to report the ambient environment conditions during the main experiment.  
In Figure 18, the corresponding percentage of water content in the stone wool is plotted in relation to the days passed since the wetting took place using the TEROS 10 sensor. It must be noted that the volumetric water content decreased by an almost fixed rate and after two weeks reached the drought level.  
These first results provided using the specific measurement methodology pave the way for a more meticulous study of the behavior of stone wool subtraces, which will be among our research priorities in the near future.  
Figure 17: Modified component arrangements for the provision of gravimetric proofs against the regular data harvested by the capacitance-based soil monitoring instruments.
# Training Spiking Neural Networks with Metaheuristic Algorithms
## 5 Discussion
The accuracy of SpikeProp was 97%, which was  
Figure 7: Classification rate of the training and testing results for seven datasets, including (**a**) Iris, (**b**) Cancer, (**c**) Diabetes, (**d**) Liver, and (**e**) Wine. around 0.68% lower than our best model. SpikeTemp, a rank-order based learning method for SNNs, however, obtained the lowest accuracy of 92.1%. For the Diabetes dataset, our best model scored 77.52%, which was higher than the scores for all the other models. For the Wine dataset, the classification accuracy of our proposed model was 95.91%, but 0.9% lower than SpikeProp. For the Liver dataset, our best model achieved an accuracy of 68.71%. The accuracies of SpikeProp and multi-SNN were 65.1% and 64.7%, respectively. SpikeTemp and Growing-Pruning (GP) obtained the lowest accuracies of 55.2% and 59.79%, respectively. We also compared the efficiency of the proposed algorithm with some existing non-spiking models for solving classification problems with medical data. [PERSON] et al. (2021) presents a thermogram-based Computer-Aided Detection (CAD) system for breast cancer detection [70]. In this CAD system, the Random Subset Feature Selection (RSFS) algorithm and a hybrid of the minimum Redundancy Maximum Relevance (mRMR) algorithm and GA with the RSFS algorithm are utilized for feature selection. The experimental results demonstrate that using the RSFS algorithm for feature selection and kNN and SVM algorithms as classifiers have 85.36% and 75% accuracy, respectively. Additionally, using hybrid GA and RSFS algorithms for feature selection and kNN and SVM algorithms for classifiers yielded 83.87% and 69.56% accuracy, respectively. In another study, [PERSON] et al. (2021) proposed a novel segmentation method for breast cancer detection using infrared thermal images [71]. The evaluation results showed that the average Dice similarity coefficient, Jaccard index, and Hausdorff distance in the FCM segmentation algorithm were 89.44%, 80.90% and 5.00, respectively. These values were 89.30%, 80.66%, and 5.15 for the MS segmentation algorithm, and 91.81%, 84.86%, and 4.87 for the MGMS segmentation algorithm. [PERSON] et al. (2018) proposed a hybrid classification optimization method for improving ANN classification accuracy [72]. Three optimization approaches named GA, PSO, and Fireworks Algorithm (FWA) were used in this work. They achieved an accuracy of 98.42% on the breast cancer datasets. In general, our proposed approach demonstrated competitive accuracy on the UCI datasets. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
# A Virtual Network Resource Allocation Framework Based on SR-IOV
## 5 Evaluation
### Network Latency  
The following is a performance evaluation of the SR-IOV virtual network resource allocation framework. First, we evaluate the web server network latency performance. We use the benchmarking tool _Httperf_[36] to generate different types of HTTP payloads to test the server performance of the system. In this test, the Apache service was run in the server-side VM, while the _Httperf_ load in the client-side VM repeatedly requested a 10 KB Web page to the server. We have gradually increased the number of VMs, and 100 requests between the server and the client are generated by _Httperf_ within one second to measure the delay of the system request response time. Here, the system's request response time is the entire time when the client initiates a request and gets a response on the server.  
Figure 7a shows a comparison of network delays before and after optimization when the request rate is fixed at 100/s. It can be seen from the figure that the request delay using the SR-IOV-based virtual resource allocation framework is smaller than the default scheduling strategy of KVM. When the number of VMs is greater than or equal to 32, the optimized system average request delay is reduced by more than 26%. In particular, when the number of VMs is 48, the optimized average request latency is reduced by 94% compared to the default scheduling policy.  
To further verify our experimental results, we fixed the number of VMs to 32, gradually increasing the client request rate from 100/s to 1000/s and measured the response latency of client requests to the server. Figure 7b shows a comparison of network latency between before and after optimization when the number of VMs is fixed to 32. It can be seen from the graph that using SR-IOV-based virtual resource allocation framework can effectively reduce the system request response time. When the client request rate is greater than 100/s, the optimization can reduce the network delay by 21% to 58% before the optimization.  
The reason the SR-IOV-based virtual resource allocation framework can effectively reduce the request response time of the system is that SR-IOV technology can reduce I/O request path of the VM. Moreover, the I/O requests of each VM are not uniformly distributed during the running of the  
Figure 7: **(a)** Comparison of network delay when the request rate is fixed. **(b)** Comparison of network delay when the number of VMs is fixed. **(c)** Comparison of bandwidth throughput. **(d)** Comparison of file transfer time.  
system but are centrally distributed in a short period of time after the VM is scheduled. At this time, the VM will be perceived by the VF Scheduler module and VF is allocated to network-intensive VMs to ensure their network performance. This indicates that in order to reduce the latency of I/O response of the VM, the time slice scheduled by the VM should be reduced, which can increase the scheduling opportunity of each VM.
## 2 Literature Survey
* _Election Parameter_: the CH is elected based on residual node energy, position of the node, or based on some calculation like ratio of average residual energy of neighbor nodes and residual energy of the node itself. * **Power Adjustment**  
Transmission power of node adjustment for communicating its data to destination node. * _Static_: Nodes' transmission power remains same _i.e._ it is neither increased nor decreased. * _Dynamic_: Nodes' either increase or decrease their transmission power according to the interaction situation. * **CH Rotation**  
Role of CH is transferred to a suitable node based on some selection parameter. * _Rotation Parameter_: CH rotation is performed either on each round, or on a decrease of some node characteristic. * \"-\": the authors don't discuss this aspect in their paper at all. \begin{table}
\begin{tabular}{c c c c c c} \hline  & **[5]** & **[10]** & **[18]** & **[20]** & **[11]** \\ \hline
**Node Type** & Homogenous & Homogenous & Both & Homogeneous & Homogenous \\ \hline
**Comm. to Sink** & **-** & Multi-hop & Multi-hop & Multi-Hop & Multi-Hop \\ \hline
**Cluster Size** & Near Equal & Near Equal & Unequal & Unequal & Unequal \\ \hline
**Cluster Designing** & Hybrid & Centralized & Distributed & Distributed & Distributed \\ \hline
**Suitability to** & Large & Small & Large & Large & Large \\
**Network Size** & & & & & \\ \hline
**CH Election** & Node Density & Node Energy & Ratio of Avg. RE of neighbor nodes and RE of Node itself & Node Degree & Node Energy \\ \hline
**Inter-Cluster** & & & CH to CH & CH-CH & CH to CH & CH-CH \\
**Comm. Style** & **-** & Direct & Direct & Direct & Direct \\ \hline
**Power Adjustment** & Static & Dynamic & Dynamic & Dynamic & Dynamic \\ \hline
**CH Rotation** & **-** & On each round & On each round & On decrease in Node density & On each round \\ \hline \end{tabular}
\end{table} 
Table 1: Comparative analysis of clustering algorithm on various network design and operational parameters.
# Technological Development and Application of Photo and Video Theodolites
## 1 Introduction
Most commonly, RTS are used for highly precise measurements such as displacements and deformation monitoring of civil engineering structures and the Earth's surface. Figure 1: Total station technological development advances. RTS with additional integrated cameras in the telescope are commonly denoted as image-assisted total station (IATS) [7]. In the literature, a number of alternative terms exist for this class of instruments [8]: photo theodolite, video theodolite/tacheometer, image-assisted photogrammetric scanning station, and others. Differentiation is often made between a video and an image or photo. This fact was valid in the past, considering analog imaging devices introduced at the end of 19 th century. Static instruments on a leveled, rotatable platform with the main function of taking images using a photogrammetric camera, supported by a theodolite for determining the elements of the exterior orientation, are called photo theodolites [9; 10]. Currently, IATS have possible applications in semiautomated object reconstruction systems [11], fully automated deformation monitoring systems [12], industrial measurement systems [13], measurements of vibration amplitudes by means of high-frequency image measurements [14], capture of additional information such as high-frequency motions or intensity fluctuations of patterns using image sensor to derive the temperature gradient of the atmosphere as a decisive influence parameter for angular refraction effects [15] and monitoring of cracks [14; 16]. This study reviews and describes the technological development of photo and video theodolites divided into phases according to the main innovations in their development (Section 2). Emphasis is placed on commercially developed instruments; however, prototypes from academia are also analyzed, since they often inspire technological developments. The basic principles of the modern IATS, camera and system calibration, and measurement procedures and data processing approaches are explained (Section 3). The application of IATS, as the successor to photo and video theodolites, is presented through experimental and in situ studies previously performed (Section 4). Lastly, a discussion with the main conclusions from conducted studies is presented.
# Unsupervised Anomaly Detection for Time Series Data of Spacecraft Using Multi-Task Learning
## 3 Materials and Method
### Multi-Tasks and Joint Learning
#### 3.3.4 Task 4: Joint Latent Representation-Based Data Prediction  
Task 4 uses an LSTM network to output predictions from the joint latent representation learned in Tasks 2 and 3. Here, \(z_{input}\) is the Task 4 input, with \(z_{input}=[z_{ae},z_{we}]\); \(\sigma_{\text{Task4}}\in\Re^{M}\) denotes the output; and \(\theta_{4}\) are the parameters to be trained for the Task 4 network, such that  
\[\sigma_{\text{Task4}}=P_{\theta 4}(X). \tag{19}\]  
Then, the difference between the predicted and expected values of the network, \(\sigma_{\text{Task4}}\) and \(Y_{t}\), respectively, is taken as the Task 4 error. This can be calculated based on the MSE, where  
\[Loss4=L(\theta_{1},\theta_{2},\theta_{4};X)=\sum\limits_{i=1}^{N}{(\sigma_{ \text{Task4}}-Y_{t}{}^{(i)})}^{2}. \tag{20}\]  
The above four tasks are jointly trained to capture the spatial and temporal correlations in the data. For the input \(X\), the joint loss function is  
\[Loss_{total}=Loss1+Loss2+Loss3+Loss4. \tag{21}\]
# Interval Uncertainty Optimization Method for Electromagnetic Orbital Launcher
## 3 Uncertainty Analysis Based on Interval Theory  
### Description of the Interval Optimization Model  
According to interval mathematics, the interval number \(A^{I}\) is defined as  
\[A^{I}=[A^{L},A^{R}]=\{x|A^{L}\leq x\leq A^{R},x\in R\} \tag{1}\]  
where \(A^{L}\) and \(A^{L}\) denote the lower and upper bounds of the interval, respectively. \(A^{I}\) can also be defined as  
\[A^{I}=\langle A^{c},A^{w}\rangle=\{x|A^{c}-A^{w}\leq x\leq A^{c}+A^{w},x\in R\} \tag{2}\]  
where \(A^{c}\) and \(A^{w}\) denote the midpoint and radius of the interval \(A^{I}\), respectively:  
\[A^{c}=\frac{A^{L}+A^{R}}{2} \tag{3}\]  
\[A^{w}=\frac{A^{R}-A^{L}}{2} \tag{4}\]  
The geometric description of the interval is shown in Figure 3.  
Using the interval number to describe the uncertainty of the parameters, the nonlinear interval number optimization problem of the general form (formulated and transformed below using the single-objective problem as an example, and similarly for the multi-objective problem) can be formulated as  
\[\min_{x}f(X,U) \tag{5}\] \[s.t.\quad\left\{\begin{array}{l}X\in\Omega^{n}\\ U_{i}\in U_{i}^{I}=[U_{i}^{L},U_{i}^{R}],i=1,2,\cdots,q\end{array}\right.\]  
where \(X\) is the \(n\)-dimensional design vector with values ranging from \(\Omega^{n}\). \(U\) is the \(q\)-dimensional interval uncertainty vector, whose values are in the range of \(U^{I}\). \(U_{i}\) is the \(i\)-th uncertainty vector. \(f\) is the nonlinear objective function on \(X\) and \(U\).  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Structure Parameter/mm** & **Range of Values** & **Step Length** \\ \hline Armature tail width \(x_{1}\) & 7.0\(\sim\)10.0 & 0.5 \\ Armature length \(x_{2}\) & 9.0\(\sim\)12.0 & 1.0 \\ Armature tail length \(x_{3}\) & 5.0\(\sim\)8.0 & 1.0 \\ Armature tail height \(x_{4}\) & 6.0\(\sim\)8.0 & 0.5 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Value ranges of design parameters.  
Figure 3: Geometric description of interval.  
In the optimization process, due to the existence of \(U\), the possible values of \(f(X,U)\) under any determined \(X\) will be an interval, so the traditional deterministic optimization methods cannot solve the problem directly. Therefore, it is necessary to transform the uncertainty optimization problem to solve the nonlinear interval number optimization problem.
## 4 Results and Discussion
### Systems' Performance
In addition, Figure 8a-c show that the MGS-II system has the best performance rather to the MGS-I system within the studied range of MR. As shown in Figures 5-8 and Table 4, The maximum GOR\({}_{\rm{HDH}}\), \(n_{\rm{ORC}}\), A\({}_{\rm{solar}}\), TGOR, STG, and STGP of the MGS-II system within the ranges of all studied parameters are 0.21,15.34 %, 1273 m\({}^{2}\), 0.6303, 3.824 kWh/m\({}^{2}\), and 0.149 USD/m\({}^{2}\), respectively.