esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Illustrative Examples
### Example: Single Variable and Constant-Stress Loading  
Let us assume that there are 100 semiconductor prototypes at the development stage, and there is no prior reliability information about the developed products. The degradation characteristic is repeatedly observed, and the failure mechanism is assumed to follow the Arrhenius model. The maximum level and use condition are specified as 125 \({}^{\circ}\)C and 55 \({}^{\circ}\)C, respectively, by the test specification. That is, by Equation (1), the temperature levels 125 \({}^{\circ}\)C and 55 \({}^{\circ}\)C are standardized as \(s_{0}\)(=0) and \(s_{M}\)(=1), respectively. The available test time is 504 h, and the measurements are conducted 21 times at equally spaced intervals.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Example** & **4.1** & **4.2** & **4.3** & **4.4** \\ \hline Number of stress variable & 1 & 2 & 1 & 2 \\ Parameter-stress relationship & Arrhenius law & Generalized Eyring & Arrhenius law & Generalized Eyring \\ Stress loading method & constant & step & constant & step \\ \hline \hline \end{tabular}
\end{table}
Table 1: Description of the examples.  
In this example, in the first phase of estimating the prior reliability information, 10 of the 100 units (as planned by the stakeholder department) are used for the design check under the normal use condition. In addition, eight units (as specified in the test document) are used for the HTOL test with a planned duration of three days, as stated in the internal specifications of the private electronic company. In addition to the above 18 units used, 2 units are additionally loaded into the HTOL test chamber to meet the D-optimality condition of Lemma 1, as in [PERSON] et al. [4]. This three-day test is regarded as the test for estimating the prior reliability information. The degradation data are available, and the model parameters for selecting the PADT design for the second phase are estimated using the MLE method. Then, a reliability engineer selects an additional seven days of testing to obtain precise reliability information. According to the test plan presented in [PERSON] et al. [4], it is assumed that 54 units are additionally loaded at the normal use condition and 1 unit is additionally allocated to the maximum stress level (125 \({}^{\circ}\)C). Furthermore, 25 units are tested at the intermediate stress level (95.1 \({}^{\circ}\)C) for checking the model's validity. Figure 5 outlines the example.  
According to Equation (1), the drifts of the mean degradation path model under the normal use condition and the maximum stress level are \(\eta(s_{0})=\exp(\delta_{0})\) and \(\eta(s_{M})=\exp(\delta_{0}+\delta_{1})\), respectively. Then, the drift ratio between the maximum stress level and the normal use condition, \(\eta(s_{M})/\eta(s_{0})\), is calculated as \(\exp(\delta_{1})\). In the example by [PERSON] et al. [4], \(\delta_{1}\) is equal to three, and it is known that the drift ratio is calculated as 20.09. This means that the degradation increment at the maximum stress level is approximately 20 times greater than the normal use condition.
# A Machine Learning Approach to Algorithm Selection for Exact Computation of Treewidth
## 4 Experiments and Results
### Experimental Results
#### 4.3.5 Dataset B--Principal Component Analysis  
Principal Component Analysis with three components was applied to the dataset and used to train a Random Forest classifier, which was trained and evaluated in the same way as the other Random Forest models. The three components cumulatively explained about 90% of the variance in the data; for a detailed breakdown, please refer to Table 3. The trained model had an accuracy of about **73%** compared to the baseline model's accuracy of **76.5%**. While the loss of accuracy is small, our drop-column feature analysis in Section 5 shows many sets of three features that can be used to build a model of similar or higher accuracy.
## 5 Evaluation
### Experience Results
#### 5.2.1 Overall Performance
In conclusion, under the same number of antennae and bandwidth, with the increase of load (from mid-low load to mid-heavy load), the time-frequency space resource allocation algorithm can achieve user balance on the heavy-load beam and the low-load beam, release the potential capability of MU pairing, make more rational utilization of space resources, and increase the average throughput gain of network-level users. In addition, under the same bandwidth, load, and user distribution, with the increase in the number of antennae, the larger the number of beams, the finer the beam granularity, the larger the MU gain, and the larger the average throughput of network-level users.
# A Survey on the Impact of Intelligent Surfaces in the Terahertz Communication Channel Models
## 3 THz Channel Modeling  
The THz frequency bands beyond 100 GHz and below 10 THz comprise a still largely unexplored part of the electromagnetic spectrum. They can adequately meet the demands of the 6G networks due to their multiple gigahertz of bandwidth [4]. This multi-gigahertz bandwidth opportunity can increase the number of users, provide signal location and detection, and satisfy some of the requirements of the next generation of wireless networks. As a result, THz communication will allow higher data rates with lower latencies [90]. Despite their large bandwidth, THz communication suffers from strong atmospheric attenuation, molecular absorption, and severe path loss, limiting its propagation [91]. These drawbacks make the implementation of a THz communication system very challenging.  
It can be said that part of the most substantial restrictions regarding the design of a communication system is imposed by the channel through which the signal is transmitted [92]. Thus, the increase in frequency requires an analysis of the best-explored propagation routes. Furthermore, the increasing demand for efficient communication services has led devices to operate in a system composed of different channel environments [93]. In this context, the adequate modeling of the wireless channel is essential, mainly in the THz band.  
The channel models in THz can be classified as statistical, deterministic, and hybrid, depending on the number of antennas implemented [94]. This study exploits the deterministic and statistical models of single-input single-output (SISO) and MIMO systems.
## 3 An Overview of GenAI-Driven CV Frameworks  
### Traditional CV Technologies in Smart Agriculture  
The traditional CV technological framework is anchored in classical algorithms such as image segmentation [51] and object detection [52], relying on manually engineered feature extraction techniques (e.g., texture, colour, and shape analysis) and pattern recognition architectures [53, 54]. These rule-driven systems exhibit a strong dependency on structured data, employing methods like threshold-based segmentation or edge detection for crop health monitoring [55] or feature-matching approaches for pest identification [56]. Characterised by computational robustness in controlled environments, these techniques nonetheless face inherent limitations, including environmental sensitivity (e.g., fluctuating illumination, occlusions) and constrained generalisation capabilities, necessitating extensive labelled datasets for scenario-specific training [34]. While stable in closed-loop settings, their high computational cost, need for labour-intensive annotation, and inability to adapt to diverse and changing farm environments have historically made it difficult to deploy them at scale [57].  
Conventional CV methodologies underpin three critical pillars of smart agriculture: (1) Continuous field surveillance leverages image segmentation with multispectral imaging for real-time crop phenotyping [58, 59, 60], while object detection enables livestock behavioural tracking [61, 62, 63]. (2) Predictive analytics utilises feature extraction (e.g., leaf area index quantification [64, 65]) to construct yield forecasting models, augmented by temporal data analysis for disease risk prediction [66, 67]. (3) Automated control systems integrate visual navigation (e.g., path planning for unmanned ground vehicles) with sensor fusion to execute precision farming operations. For instance, drone-mounted CV algorithms facilitate large-scale field inspections [68, 69], while edge computing platforms synthesise visual inputs to optimise irrigation scheduling [70, 71]. Though limited by their reliance on handcrafted features in complex agroecological contexts, these technologies established the foundational infrastructure for Agriculture 4.0, enabling data-driven decision cycles that transitioned farming from empirical practices to precision resource management.
## 2 Monitoring of Nutritional Status in CKD Patients: Affected Parameters
### Blood Chemistry Parameters  
In hemodialysis patients, results obtained for a series of blood chemistry parameters are not deemed as reliable as when evaluated in other chronic conditions. Malnutrition may be indicated at levels of albuminemia <3.8 g/dL and pre-albumin <30 mg/dL. The latter, also referred to as Transthyretin (transport protein for thyroxine and retinol), is a rapid marker for malnutrition based on serial measurements obtained over several days, although levels of this protein are influenced by age, sex, CRP, IL-6, Charlson Score and visceral body fat [32; 33]. Total cholesterol < 100 mg/dL and decreases in non-HDL-cholesterol and non-HDL/HDL cholesterol ratio (including triglyceride-rich lipoproteins) have been paradoxically associated with increased all-cause and cardiovascular mortality in patients undergoing incident hemodialysis [34]. However, numerous other indicators may also not constitute reliable indicators, as an example: (a) low serum transferrin, estimated by total iron-binding capacity (TIBC) is influenced by iron deficiency, inflammation, poor quality of life in patients on hemodialysis [35], (b) creatinine is heavily influenced by muscle mass volume, hemodialysis adequacy, residual renal clearance, hypercatabolism by dialysis, protein food intake prior to sampling (e.g., previous meal), particularly when blood is drawn following the longer interdialytic period or during afternoon HD sessions [36], (c) serum leptin is one of the parameters underlying the onset of anorexia in hemodialysis patients, but cannot be considered an important correlation factor due to significant association with inflammation [37; 38], (d) metabolic acidosis, together with low caloric intake, elicits muscle proteolysis, reducing the sensitivity of cells to insulin, boosting the presence of molecules such as ghrelin and leptin that act on the central nervous system (CNS), which in turn increase resting energy expenditure [39], and (e) lymphocytopenia may represent a confounding factor due to the frequent presence in HD patients of a sub-chronical disease-causing a decrease in lymphocyte count, including primary immune deficiencies and immune deficiencies secondary to malnutrition or zinc deprivation, excess catabolism, immunosuppressive therapy, HIV infection, systemic lupus erythematosus, certain viral infections, lymphoma, renal insufficiency, and idiopathic CD4 lymphocytopenia [40].
# Rare Earth Magnet Recycling and Materialization for a Circular Economy--A Korean Perspective
## 4 Liquid Metal Extraction (LME)
Finally, rare earth elements diffused into the Mg area through the path provided by the Mg (black phases in the reaction zone) inside the magnet. It can be seen from the XRD patterns that Mg successfully decomposed the inherent phases of the NdFeB magnet (RE\({}_{2}\)Fe\({}_{14}\)B, RE-rich, and RE-oxide), which can be seen in the XRD patterns of the magnet area, while rare earth elements diffused into Mg to form intermetallic compounds, i.e., Mg\({}_{12}\)RE (gray phases in the Mg area). The solid-liquid reaction mechanism can be explained by the diffusion interaction between Mg and the magnet, in which Mg exists in a liquid state at 900 \({}^{\circ}\)C, while the magnet remains as a solid. Mg liquid metal atoms diffuse into the magnet to form a diffusion layer on the surface, which in turn alters the chemical composition of the surface layer. This change of composition of the surface layer decreases the surface energy and melting point of the solid, allowing it to transform into liquid state by melting. At a low solubility limit in the solid phase, the diffusion from the melt into the solid proceeds mainly along structure defects (grain and sub-grain boundaries, etc.). The change of the chemical composition, as a result of such selective diffusion, causes the formation of liquid phases along grain boundaries [43,44]. The LME reaction parameters were optimized as part of the ReMaT process. Figure 8 shows the extraction efficiency results from the LME experiments for Nd and Dy at 900 \({}^{\circ}\)C  
Figure 7: Microstructure and XRD patterns of the coupled Mg–magnet sample after 3 h of LME reaction at 900 \({}^{\circ}\)C.  
for a magnet/Mg ratio of 1:10. An extraction efficiency of 100% was observed for Nd at an extraction time of 6 h, while an extraction efficiency of 94% was achieved for Dy at an extraction time of 48 h. Previously, the highest reported extraction efficiency for Dy in LME experiments was 74%, which was enhanced to 94% in our process. The explanation for this improvement is provided elsewhere [45]. An overall extraction efficiency for REE of 97% was achieved as part of the ReMaT process.
## 3 Results and Discussions
### The Structure of U-Turn Motivation and the Demographic Characters of Each Respondents' Group  
In the previous section, we discussed the multilayered relationship between U-turn motivation and living environments. In order to appropriately promote people's U-turn motivation by applying this model, it is necessary to examine the characteristics of each last node and grasp whether each group's U-turn migrations can improve the population disparities in Japan. For this purpose, we first assessed the characteristics of each last node while focusing on the relationship between the demographic attributes of individuals and the structure of U-turn motivation derived from the evaluations of living environments. The results of this analysis would provide local governments with useful information about which living environments they should develop for attracting people with specific attributes that contribute to the population sustainability (i.e., appropriate population size and age distribution to maintain the urban functions) of the regions. Table 6 shows the distribution ratio for the demographic characteristics (age, gender, family structure, and birth order, i.e., eldest son or not) of individuals in the last nodes of the decision tree. We focused on groups that were statistically significant, or those considerably deviated appreciably from the distribution ratio for a group of all respondents. The notable demographic characteristics are summarized in Table 7.  
In Node 3, there were more young people aged under 40, women, and married couples that did not live with children, than in other nodes. The prevailing level of U-turn motivation was low due to negative evaluations of the climate in respondents' hometowns, despite the stronger attachment to hometowns than to current residences. A total of 78.3% of respondents in Node 3 had a car in their household, and most people did not live with children. It was assumed that approximately 60% of their parents were not older elderly and were not required nursing care yet (assuming an average age difference of thirty years). Respondents at Node 3 tended to base their U-turn motivations on only abstract ideas related to attachment or climate and were not concerned with the more practical living conditions--such as childcare, support for parents, and transportation--because they had fewer problems in general.
## 2 Cross-Domain Collaborative Task Allocation Model
### Establishment of Constrained Objective Functions for Cross-Domain Collaborative Task Allocation  
In the context of the cross-domain cooperative task, the air relay control agent had an extremely high maneuverability, and the relay control load agent hit the target as the core task. In the actual task process, after developing the control allocation scheme of the load agent, the air relay control agent had enough time to reach the predetermined position. Therefore, in order to simplify the model architecture, the optimal scheme of target allocation was used as the precondition of control allocation, and the control allocation results were considered to converge in all the feasible solution sets of target allocation.  
The cross-domain collaborative task allocation model is shown in Equation (16).  
\[\begin{split}\min\biggl{\{}\sum\limits_{i=1}^{3}\rho_{i}\overline{ \overline{p}_{i}}\biggr{\}}&=\min\Biggl{\{}\rho_{1}\times(-\sum \limits_{j=1}^{c}\overline{v_{j}}\times E_{j}\times(1-\prod\limits_{i=1}^{d}(1- p_{ij})))+\rho_{2}(\sum\limits_{i=1}^{d}\sum\limits_{j=1}^{c}\frac{D_{ij}}{ \overline{v}})+\rho_{3}(\sqrt{\sum\limits_{n=1}^{a}\left[\frac{S(n)}{r_{n}} \right]^{2}})\Biggr{\}}\\ &\max\biggl{\{}\prod\limits_{i=1}^{3}Q_{i}\biggr{\}}=\min\biggl{\{} -\prod\limits_{i=1}^{3}Q_{i}\biggr{\}}=\min\{-Q_{1}\times Q_{2}\times Q_{3}\} \\ &\text{s.t.}\left\{\begin{array}{l}\text{1}\sum\limits_{i\geq 1}^{d}x_{ij} \in N^{+},x(i,j)\in\{0,1\}\\ \text{2}\sum\limits_{i=1}^{d}\sum\limits_{j=1}^{c}x(i,j)\leq d,\sum\limits_{i= 1}^{d}\sum\limits_{k=1}^{b}y(i,k)\leq d\\ \text{3}\sum\limits_{j=1}^{c}x(i,j)=1,\sum\limits_{k=1}^{b}y(i,k)=1,i=1,2, \cdots,d\\ \text{4}\sum\limits_{i=1}^{d}x(i,j)\leq m,j=1,2,\cdots,c\\ \text{5}\sum\limits_{i=1}^{d}y(i,k)\leq n,k=1,2,\cdots,b\\ \text{6}\sum\limits_{i=1}^{d}y(i,k)\leq D_{M}\end{array}\right.\end{split} \tag{16}\]  
According to the importance of each element in the model and expert experience, the AHP method was used to assign weights to each objective function value in the objective allocation. Specifically, the weights were assigned as follows: \(\rho_{1}=0.5,\;\rho_{2}=0.2,\;\rho_{3}=0.3\). The overall model framework incorporates multiple criteria decision-making methods to achieve balanced and optimized task allocation.
# A Fairness Index Based on Rate Variance for Downlink Non-Orthogonal Multiple Access System
## 4 Power Allocation under Fairness Constraints
### Power Allocation Intra Cluster  
The power allocation intra cluster can be expressed as:  
\[\begin{array}{ll}P1&\max\limits_{\alpha_{n}^{s},\alpha_{n}^{w}}R_{c-n}=R_{c -n}^{s}+R_{c-n}^{w}\\ s.t.&C1:\alpha_{n}^{s}+\alpha_{n}^{w}=1\\ &C2:0<\alpha_{n}^{s}\leq 0.5\\ &C3:0.5\leq\alpha_{n}^{w}<1\\ &C4:F_{c-n}\geq\varphi_{c-n},n\in\left[1,N\right]\end{array} \tag{16}\]  
According to Equation (13), \(F_{c-n}\) can be expressed as:  
\[F_{c-n}=2/\left(x+\frac{1}{x}\right) \tag{17}\]  
where \(x=R_{c-n}^{s}/R_{c-n}^{w}\), \(x\in\left(0,+\infty\right)\). When \(x=1\), \(F_{c-n}=1\), set \(\alpha_{n}^{s}=\alpha_{equal}\) at this time. Let \(\alpha_{n}^{s}=0.5\), then set \(F_{c-n}=\varphi_{mid}\). If \(\alpha_{n}^{s}\in\left(0,\,\alpha_{equal}\right),F_{n}\) monotonically increases, and \(F_{n}\in\left[0,\,1\right]\). If \(\alpha_{n}^{s}\in\left[\alpha_{equal},0.5\right)\), \(F_{n}\) monotonically decreasing, and \(F_{n}\in\left(\varphi_{mid}.1\right]\). According to the principle of PD-NOMA, the greater the power allocated to strong channel gain user, the smaller the fairness and the greater the throughput in the cluster. Combined with the above analysis, if \(\varphi_{c-n}\in\left[0,\varphi_{mid}\right]\), the throughput in the cluster is the largest when \(\alpha_{n}^{s}=0.5\); if \(\varphi_{c-n}\in\left(\varphi_{mid},1\right]\), then the throughput in the cluster is the largest when \(F_{c-n}=\varphi_{c-n}\). According to Equation (16), we can get:  
\[x=\frac{1}{\varphi_{c-n}}\times\left[\left(1-\varphi_{c-n}^{s}\right)^{\frac{1 }{2}}+1\right] \tag{18}\]  
According to \(R_{c-n}^{s}/R_{c-n}^{w}=x\), \(\alpha_{n}^{s}\) can be obtained.  
Based on the above analysis, the intra cluster power allocation algorithm is as follows.
# IAACaaS: IoT Application-Scoped Access Control as a Service +
## 4 IoT Application-Scoped Access Control as a Service
### The Identity Provider
#### 4.2.3 Groups Management  
An interesting feature is that groups of devices (or users) can be created, to allow more complex authorization scenarios. Granting a _Role_ to a group of devices, rather than to a single device, has the benefit of linking the given _Role_ to the devices belonging to the group. For instance: devices representing the streetlights in a park \(A\) in a smart city application must be the only ones able to write data in the _Service_ about park \(A\); therefore, when one of those devices moves to a park \(B\) (e.g., a subdivision is created), removing it from group \(A\) is enough to deny its access to resources of its past park.
## 3 Simulation Examples
### Residual STFT-Based Mode Shape Extraction
#### 3.2.2 Effect from Vehicle Velocity  
As the derivations in Section 2.1 indicate, the residual responses between the contact points contain three main frequency components, including the driving frequency (\(j\pi V_{e}\)/L) and the two shifted frequencies (\(\omega_{j}-j\pi V_{e}\)/L and \(\omega_{j}+j\pi V_{e}\)/L) centered at the beam natural frequency (\(\omega_{j}\)). So, the velocity (\(V_{e}\)) is critical to the distributions of these frequencies. Three different velocities are used including the slow speed of 2.5 m/s (9 km/h), the intermediate speed of 5 m/s (18 km/h), and the relatively high speed of 10 m/s (36 km/h) with the roughness profile of Class A.  
When the tractor with the trailers moves on the bridge with different velocities, the duration varies as 10 s for the velocity of 2.5 m/s, 5 s for the velocity of 5 m/s, 2.5 s for the velocity of 5 m/s, and 2.5 s for the velocity of 10 m/s. A longer duration could provide more response data to the STFT. However, the ratio \(q_{j}=j\pi V_{e}/(L\omega_{j})\) in the contact point's response expressions (Equation (7)) will decrease with the lower velocity and then reduce the contribution from the components (\(\omega_{i}-j\pi V_{e}/L\) and \(\omega_{i}+j\pi V_{e}/L\)) about the beam natural frequencies. As shown in Figure 7, all extracted first and second mode shapes using these three velocities are very close to the analytical ones. There are some moderate deviations around the peaks of the third mode shape, especially when using the lower velocity (2.5 m/s). As a contrast, the relatively high speed of 10 m/s could excite the beam from higher frequencies. However, due to it having the shortest duration among the three velocities, only the beam mode shapes from 3.5 m to 21.5 m of the beam length could be extracted using STFT. It is similar to using a larger window in the previous section. Therefore, a medium velocity of 5 m/s is recommended and used in the following work.  
Figure 7: The first three beam mode shapes using the residual STFT-based extraction under velocities of 2.5 m/s, 5 m/s, and 10 m/s, compared to the analytical first mode shape (**a**), the second mode shape (**b**), and the third mode shape (**c**), as well as the MAC values.
## 5 Discussion
### Discussion of the Consistency between the OWCM-/GM-Derived SM and RFSM
#### 5.1.1 Spatial Consistency
In general, the distribution of OWCM-derived SM metrics is notably superior to that of the corresponding GM-derived SM products, and the ATA-OWCM SM products perform the best overall.