esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Case Study
### TEP Dataset Analysis
#### 3.1.1 Dataset Overview
The structured data thus prepared serves as the foundation for ATASML to apply its meta-learning and adversarial augmentation techniques, aiming to enhance the model's diagnostic performance. For the purposes of this study, each of the 15 fault categories within the TEP dataset was dissected into 23 distinct windows, culminating in a total of 345 unique data intervals for in-depth analysis. This segmentation provides a comprehensive dataset for ATASML to train and validate its fault-diagnosis capabilities. Moreover, to concentrate the analysis on the most pertinent data for fault detection, the initial 8 h segment of normal operation data was excluded from the testing subset. This decision ensures a focused evaluation on the segments where fault conditions are present, enabling a more accurate assessment of ATASML's effectiveness in identifying and diagnosing process anomalies. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Fault ID** & **Fault Description** & **Nature of Disturbance** \\ \hline IDV1 & A/C feed ratio, B composition constant & Step change \\ IDV2 & B composition, A/C ratio constant & Step change \\ IDV3 & D feed temperature & Step change \\ IDV4 & Reactor cooling water inlet temperature & Step change \\ IDV5 & Condenser cooling water temperature & Step change \\ IDV6 & A feed loss & Step change \\ IDV7 & C header pressure loss & Step change \\ IDV8 & A, B, C feed composition & Random variation \\ IDV9 & D feed temperature & Random variation \\ IDV10 & C feed temperature & Random variation \\ IDV11 & Reactor cooling water inlet temperature & Random variation \\ IDV12 & Condenser cooling water valve operation & Random variation \\ IDV13 & Reaction kinetics alteration & Slow drift \\ IDV14 & Reactor cooling water valve operation & Sticking \\ IDV15 & Condenser cooling water valve operation & Sticking \\ \hline \hline \end{tabular}
\end{table} 
Table 2: TEP Fault Categories Applied in ATASML Fault Diagnosis.
## 3 Proposed Approach
### Feature Selection
#### 3.3.2 Feature Selection Decision  
When contrasted to a single decision-maker, the ensemble learning technique is analogous to the decision-making process of numerous decision-makers working in conjunction with one another [52,53]. The weighted majority voting is a kind of ensemble learning, which is realized using a linear combination of the voting results of multiple base classifiers. Therefore, this paper drew on the idea of weighted voting in ensemble learning and considers feature selection as a weighted voting process to select key features for the scenarios.  
As the goal functions provided in this work are intended to be reduced, dispatching rules with lower fitness have higher voting weights. Therefore, a dispatching rule's \"voting weight\" should be a function that decreases monotonically with its fitness. Equations (12)-(15) describes the calculation.  
\[w(r)=max\bigg{\{}\frac{u(r)-u_{min}}{u_{max}-u_{min}},0\bigg{\}} \tag{12}\]  
\[u(r)=\frac{1}{1+fitness(r)} \tag{13}\]  
\[u_{max}=\frac{1}{1+min(fitness(r)|r\in R\,)} \tag{14}\]  
\[u_{min}=\frac{1}{1+max(fitness(r)|r\in R\,)} \tag{15}\]  
The main process of weighted voting was as follows:  
(1) In the job-shop scenarios, the contribution of each feature \(t_{i}\) to the dispatching rule \(r,r\in R\) was calculated according to the Equation (11). A contribution threshold \(\theta\) was used to set a positive value between (0, 1) to prevent the selection of weakly correlated features;  
(2) Next, we compared the contribution \(C^{r}_{t_{i}}\) with the threshold \(\theta\), if \(C^{r}_{t_{i}}\geq\theta\), after which the dispatching rule \(r,r\in R\) was allowed to vote for feature \(t_{i}\). Otherwise, we ruled \(r,r\in R\) votes as being votes against the feature \(t_{i}\);  
(3) Finally, we calculated the voting results of all dispatching rules in \(R\) for feature \(t_{i}\)\(T_{\text{vote}}(t_{i}|R)=\sum_{r=1}^{|R|}T_{\text{vote}}(t_{i}|r)\). Then, the feature \(t_{i}\) was selected, provided the weighted voting for it was greater than the weight against it; otherwise, it was not selected.
## 4 Analysis of the Test Results
The values of the confinement factor are presented in Figure 14.9 The concrete contribution ratio (CCR) was used as a way to estimate the effect of enhanced strength of the core concrete in CFST, as in [40], and it is defined as:  
Figure 12:0  
where \(N_{u}\) is the test ultimate force of the composite column, while \(N_{us}\) is the test ultimate force of a hollow steel tube. Since the hollow steel column was not tested experimentally, its ultimate force was calculated from expression: \(N_{us}=f_{y}A_{s}\). The relationship between the confinement factor and the concrete contribution ratio is shown in Figure 19a. From the presented values, it was observed that the values of CCR in strengthened columns filled with high-strength concrete were greater than values for specimens filled with lower strength concrete. Oppositely, with the rise of the confinement factor, the contribution of the core concrete strength on values of the CCR decreased. A lower degree of confinement provided to the concrete by the steel tube was caused by high concrete strength, and in general, a composite column with a greater confinement factor tends to have pronounced ductile behavior, as it was described in [41]. The influence of the test parameters for strengthened columns in this research is presented in Figure 19b. The column crushing load tends to increase as the compressive strength of fill concrete increases.
# Fluidization Behavior of Soft Soil Induced by Shield Construction
## 2 Methodology
### Shield Shell-Soil Shear Flow Model  
The flow behavior of the soil around the shield shell occurs when the shear stress of the soil (\(\tau\)) caused by the applied thrust reaches the initial yield stress \(\tau_{c}\), and the sheared region increases with an increasing thrust, as shown by the development of the soil phase in Figure 1. Considering that the soil is homogeneous and the shear velocity is evenly distributed around the shield shell, the sheared region caused by the shield advancing expands evenly along the radial, with a series of concentric iso-velocity shells being formed. To describe the above characteristics, the flow layer can be assumed to be a sleeve mode equiaxed and parallel to the shield shell.  
A shield shell-soil shear flow model was established, as shown in Figure 2. Assuming that the shield advances at a uniform speed, the applied force should balance with the  
Figure 1: Soil phase transformation.  
shear resistance on the flow layer. Therefore, the force balance equation in the longitudinal direction of the cylindrical soil per unit length within the radius \(r\) in the sheared region was obtained:  
\[F=2\pi r\tau\tau \tag{2}\]  
where \(F\) is the thrust applied on the soil around the shield per unit length.  
The radius of the sheared region boundary \(r_{c}\) corresponds to the position where the flow velocity is zero. When \(r\) tends to \(r_{c}\), the shear stress approaches \(\tau_{c}\). Substituting Equation (2), the radius of the sheared region boundary \(r_{c}\) is as follows:  
\[r_{c}=\frac{F}{2\pi\tau_{c}} \tag{3}\]  
Substituting Equations (2) and (3) into Equation (1):  
\[\mathrm{d}u=-\frac{F}{2\pi K}\bigg{(}\frac{1}{r}-\frac{1}{r_{c}}\bigg{)} \mathrm{d}r \tag{4}\]  
At the position of the shield skin \(R\), the velocity of the soil is \(Vs\), and at the radius \(r\) of the sheared region, the velocity is \(u\). Substituting Equation (4):  
\[\int_{V_{s}}^{u}\mathrm{d}u=-\int_{R}^{r}\frac{F}{2\pi K}\bigg{(}\frac{1}{r}- \frac{1}{r_{c}}\bigg{)}\mathrm{d}r \tag{5}\]  
where \(R\) is the radius of the shield shell.  
The velocity distribution function solved by the integral is as follows:  
\[u=-\frac{F}{2\pi K}\bigg{(}\ln\frac{r}{R}-\frac{r-R}{r_{c}}\bigg{)}+\ V_{s}\quad r \in[R,r_{c}] \tag{6}\]  
Substituting \(r=r_{c}\) and \(u=0\) into Equation (6), the speed of the shield advancing is obtained:  
\[V_{s}=\frac{F}{2\pi K}\bigg{(}-1+\frac{2\pi\tau_{c}R}{F}+\ln\frac{F}{2\pi\tau_ {c}R}\bigg{)} \tag{7}\]
# The FaaS-Based Cloud Agnostic Architecture of Medical Services--Polish Case Study
## 3 Materials and Methods
### Fission Architecture  
Fission is based on a few custom resource definitions, and thus it is well integrated with Kubernetes. The main components of Fission are: the _Router_, _HTTP Trigger_, _Message Queue Trigger_, _Timer_, _Controller_, _Executor_, _Builder Manager_, _Builder Pod_ and _StorageScr_. The _Controller_ is responsible for creating Fission CRD objects. The functions available through Fission can be invoked by the _Router_, _Message Queue Trigger_ and _Timer_ as a result of incoming HTTP requests, messages delivered to a queue or a topic occurrence of a time event (generated by a Kubernetes _Cron job_). Fission can work in two specific modes ([[https://fission.io/docs/architecture/executor/](https://fission.io/docs/architecture/executor/)]([https://fission.io/docs/architecture/executor/](https://fission.io/docs/architecture/executor/)), accessed on 13 April 2022):  
* _PoolManager_;
* _NewDeployment_.  
When operating in _PoolManager_ mode, functions are available as archive packages of source code (developed in Java, JavaScript, Go, etc.). The _Builder Manager_ component uses the _Builder Pod_ to fetch file archives from _StorageScr_ and builds executable artifacts and uploads them to a shared volume.  
In response to some requests coming from the _Router_, the _Executor_ should provide the address of the _Function_ (Figure 1) which is exposed by some _Pod_.  
Those _Pods_ may be already instantiated/available (a warm start of the _Function_) or not (a cold start of the _Function_).  
The _PoolManager_ is responsible for holding a few so-called _Generic Pods_, whereas the _Executor_ has to provide a _Function_ to the _Router_. This process performed by the _Executor_  
Figure 1: Sequence Diagram: _Router_ to _HTTP trigger_ and _Router_ to _Executor_.  
is named the _specialization_ of one of the _Generic Pods_. The _Executor_ injects a proper package (the one previously prepared by the _Builder Pod_) into a running _Pod_ and exposes it to the _Router_.  
This mode (i.e., Pool Manager mode) allows having one poll of _Generic Pods_ for the usage for any kind of _Function_, which is a big advantage. However, _Pods_ should stay available all the time (causing a significant utilization of memory), and (what is more important) the process of _Pod_ specialization takes a significant time (which may result in unacceptable timeouts for clients).  
Those above-mentioned weaknesses can be overcome by using the _NewDeployment_ mode. Here, the _Executor_ spins up a dedicated _Pod_ (or _Pods_) for the _Function_ required by the _Router_. The _Executor_ creates/applies a _Deployment_, _Pod_, _Service_ and _HPA_ for a needed _Function_ (Figure 2). The number of _Pods_ depends on _HPA_ settings and can be adapted to a current load of _Function_ calls.
## 4 Security Analysis
### Informal analysis
#### 4.2.7 Denial of Service Attacks (DoS)  
In the DoS attack, attackers hijack network resources to make them unusable by network entities. In the protocol, each message sent contains a timestamp, and both GCS and UAV verify the validity of the timestamp. In addition, during the authentication process, the timestamp and HMAC verification are doubly authenticated before the authentication signature, which can help defend against DoS attacks.
# Saturation, Allowed Transitions and Quantum Interference in Laser Cooling of Solids
## 3 Methods and Results
### Improved Cooling via Saturation of Background Absorption in Yb:LiYF\({}_{4}\)
#### 3.1.1 Differential Luminescence Thermometry (DLT)
When the second and third terms on the right side of Equation (13) can be ignored, the thermal balance equation reduces to \(\eta_{c}=P_{abs}^{-1}cMdT/dt\). The temperature derivative with time can then be determined by applying a linear fit to the data (for temperature changes of less than 1 Kelvin or at times of less than 1 min, whichever occurs first). Examples of such fits are plotted in the insets of Figures 8 and 9. By measuring the injected, reflected, and transmitted pump power, the absorbed power can also be determined. Thus, the cooling efficiency can be measured directly from the temperature evolution measurements.
# Relay-Enabled Task Offloading Management for Wireless Body Area Networks
## 4 Proposed Resource Management Solution
### Network Initialization Phase  
The C-MEC is placed at the center of the hospital ward and a series of on-body relays are attached to a patient's clothes. Implanted sensors are located inside the human body at the predetermined positions. The C-MEC broadcasts a short message information packet to the relays, which assigns a unique ID to each. The relays store the C-MEC location information and send information packets, which contain the energy status and locations. In accordance with Reference [22], the implanted sensors broadcast short information to all relays, which are then updated with the location and energy status of the implanted devices. In this way, all relays can update the location and channel information for potential task offloading. After network initialization is complete, one round starts.
## 4 Discussion
### Further Work
#### 4.4.2 Various engineering applications  
TCCs have great application prospects in hydropower and coastal engineering. In addition to controlling the water temperature of stratified reservoirs, it can improve downstream water quality. A curtain arranged in the upper part of a river can effectively block high-nutrient flow into downstream areas, thus alleviating eutrophication and algal bloom. Curtains can also be applied to isolate water of different densities, such as fresh water and sea water, thus building marine reservoirs to support the freshwater demand for offshore engineering. With the development of material science and technology, flexible materials will be widely used in hydraulic structures.
## 3 Results and Discussion
### Sample Segregation by VOCs  
Classification tree is a potential power tool (data mining) for investigating multilevel interactions [30; 31] and classify samples. The technique was used to identify a few VOCs able to segregate the spoiled samples from those following normal fermentation from industries A and B. The rules (Table 1) were based on the values of the _clr coefficients_ corresponding to methyl 2-methylbutenal (V24) and acetone (V3) and can predict further classification. The levels of the first VOC, segregated, on the left, the normal samples from industry A with low _clr_ values, \(\leq\)-4.420. The other two groups, on the center, included butyric brings (company A) and normal samples from industry B, with an intermediate _clr_in the interval \((-4.420,\,-4.016)\) for methyl 2-methylbutenal (V24); and on the right, sulfidic and putrid olive samples, without segregation between them, with high _clr_ levels \(>-4.016)\) (Figure 3). After that, the samples with intermediate concentrations \((-4.420,\,-4.016)\) were re-assigned to their corresponding groups: butyric spoiled (industry A) (cut-off limits \(\leq-0.365\)) and normal fermentation (industry B) (Figure 3), based on the _clr_ values of acetone. The overall success using the rules mentioned above (Table 2) was 90%, with 100% in most samples (except those affected by sulfidic spoilage, considered as putrid). This misclassification agrees with the ANOVA results because of both types of spoilages' simultaneous contribution to several VOCs. The classification rules are simple and show that a few compounds could be enough for differentiating spoiled and normal olives, but the technique is not exhaustive.  
\begin{table}
\begin{tabular}{l c c} \hline \hline \multirow{2}{*}{**Nodes**} & **CODES** & **Rules** \\  & **(Prediction)** & **Rules** \\ \hline Node 1 & 2 & \\ Node 2 & 2 & If _clr._2-methyl-2-butenal (V24) \(\leq-4.42041\) then CODES = 2 in 40\% \\ Node 3 & 5 & If _clr._2-methyl-2-butenal (V24) \(-4.42041,\,-4.01629]\) then CODES = 5 in 40\% cases \\ Node 4 & 4 & If _clr._2-methyl-2-butenal (V24) \(>-4.01629\) then CODES = 4 in 20\% \\ Node 5 & 1 & If _clr._2-methyl-2-butenal (V24) \((-4.42041,\,-4.01629]\) and _clr._acetone (V3) \(\leq-0.412896\) then CODES = 1 in 20\% cases \\ Node 6 & 5 & If _clr._2-methyl-2-butenal (V24) \((-4.42041,\,-4.01629]\) and _clr._acetone (V3) \(>-0.412896\) then CODES = 5 in 20\% cases \\ \hline \hline \end{tabular}
\end{table}
Table 1: Classification tree analysis. Rules for the classification of spoiled and normal Spanish-style Manzanilla fermentation samples. Codes are as follows: 1, butyric fermentations from industry A; 2, normal fermentations from industry A; 3, sulfidic spoilage from industry B; 4, putrid spoilage from industry B; and 5, normal fermentations from industry B.  
Figure 3: Classification tree of samples, using the _clr coefficients_ of the original data set. Notice the simple prediction by just a few components.
# Conditional Temporal Aggregation for Time Series Forecasting Using Feature-Based Meta-Learning
## 3 Proposed Method
### Feature Selection  
There is a significiant range of features that can be used to describe a time series and its characteristics. When it comes to fast-moving data, a widely used set of time series features can be retrieved from the _tsfeatures_ package for R [53]. Another interesting set of features has been introduced by [PERSON] and [PERSON] [54], emphasizing statistics, frequency, autocorrelation, and diversity. Considering slow-moving data, [PERSON] et al. [55] proposed a set of features aiming to sufficiently describe lumpy series. Nevertheless, there is no objective way of determining the optimal number or set of features to be used for describing a data set, nor it is guaranteed that using the maximum amount of possible extracted features will result in better results. Therefore, in our study, we use a variety of features that, in our opinion, can sufficiently describe the essential components of the time series at hand.  
Specifically, for slow-moving data, we consider two essential features that are widely used for categorizing demand patterns [56]: the average inter-demand interval (ADI), which measures the frequency of zero instances in the data set, and the coefficient of variation (\(CV^{2}\)), which measures the variation of non-zero demand occurrences. In addition, we use some of the features proposed by [PERSON] et al. [55]. Note that the aforementioned features are calculated both for the original time series and the time series of lower frequencies derived when employing temporal aggregation. This process results in a richer set of features that not only describes the original data set, but also provides useful information for the temporally aggregated series.  
With regard to the fast-moving data, we exploit the wide variety of features provided by the _tsfeatures_ package. The features capture time series characteristics such as trend, seasonality, and stability, as well as statistical measures over the time series data, such as autocorrelation. In resemblance to the approach used for the slow-moving data, we extract the selected features at all temporal aggregation levels. The features used, along with the levels on which they are calculated for both the slow- and fast-moving data sets, are presented in Tables 15 and 16 of Appendix A.
## 3 Results and Discussion  
### Aggregates Characterization  
#### 3.1.1 Particle Density and Water Absorption  
Table 1 presents the results of the particle density and water absorption of the SSA and natural aggregates (NA), using the 10/14 fraction.  
This test demonstrates that the particle density of SSA is approximately 25% higher than that of the natural granitic aggregates, which may have a significant effect on the mix design of asphalt mixtures with SSA. Moreover, the asphalt mixture weight increases for the same area or volume of road paving application, impacting transportation costs. The water absorption of SSA is also higher than that of the natural granite aggregates due to its increased porosity, suggesting an increased amount of bitumen absorbed and the need for higher binder contents during asphalt mixture production.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameter** & **Unity** & **SSA** & **NA** \\ \hline Apparent particle density & (Mg/m\({}^{3}\)) & 3.40 & 2.69 \\ Particle density on a saturated and surface-dried basis & (Mg/m\({}^{3}\)) & 3.29 & 2.64 \\ Pre-dried particle density & (Mg/m\({}^{3}\)) & 3.35 & 2.66 \\ Water absorption & (\%) & 1.3 & 0.7 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Result of SSA and NA particle density and water absorption.  
Figure 2: AC 20 bin/base mixture with natural granitic aggregates (**left**) and equivalent asphalt mixture with 75% SSA (**right**).
## 2 Related Work
### Research on Data Aggregation Routing
Once data packets reach the sink via a path, the nodes on the path set their \"hop\" to 0, and update their route, then their neighbor nodes update their route. Thereby, the part which connects to the sink of many routing paths coincides with the path on which the \"hop\" of the nodes are set to 0, the number of the connections to the sink significantly decreases. Cluster-based WSNs is data aggregation network. In a cluster network, the nodes are divided into several clusters. A cluster consists of two types of nodes, cluster heads (CHs) and cluster member nodes. CHs collect the data packets generated in their cluster and aggregate the data and transmit it to next hop (possibly another CH). The relevant studies can be found in Ref. [61]. The research above are all classical data aggregation routing methods. There is another method based on approximate data aggregation. In our previous work, we proposed a routing method combining approximate data aggregation method with traditional data aggregation method [61]. In some applications, such as monitoring the temperature and humidity of plants on the farm, the monitored value is allowed within a certain error range. Thus, the monitoring value can be set as the value of a representative node, so it is unnecessary to transmit all the monitoring value of each node to the sink [62]. The data transmission volume of the \(n\) nodes' network can be reduced to \(1/n\). The key to this method is to elect the representative node. A simple idea is to combine a certain node (representative node) to its neighbor nodes whose monitoring values is in the error range into a set. After a round of combination, the set can be expanded to the neighbor nodes of the newly included nodes in the set. When the set stops expanding, the set of representative node establishes. All nodes in this set can be represented by the representative node. In [62], we propose a scheme to improve data aggregation along the path representative nodes route to the sink. Table 1 is presented to summarize the references