esa-sceva/satcom-chunk-collection huggingface dataset:
## 7 Sensitivity Analysis
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**No.**} & \multicolumn{4}{c}{**Weight Range**} & \multicolumn{4}{c}{**Evaluation Results**} \\ \cline{2-9}  & \(\mathbf{C_{1}}\) & \(\mathbf{C_{2}}\) & \(\mathbf{C_{3}}\) & \(\mathbf{C_{4}}\) & **Sample A** & **Sample B** & **Sample C** \\ \hline
1 & 0.179 (\(-\)0.029) & 0.282 (\(+\)0.025) & 0.293 (\(+\)0.024) & 0.246 (\(-\)0.0200) & 0.511 & 0.512 & 0.620 \\
2 & 0.181 (\(-\)0.027) & 0.297 (\(+\)0.040) & 0.27 (\(+\)0.001) & 0.252 (\(-\)0.0140) & 0.513 & 0.514 & 0.619 \\
3 & 0.237 (\(+\)0.029) & 0.237 (\(-\)0.020) & 0.19 (\(-\)0.079) & 0.336 (\(+\)0.070) & 0.515 & 0.516 & 0.618 \\
4 & 0.218 (\(+\)0.010) & 0.208 (\(-\)0.049) & 0.318 (\(+\)0.049) & 0.294 (\(+\)0.028) & 0.518 & 0.519 & 0.615 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Weight range for each criterion. stability and reliability. However, the sensitivity analysis reveals that ranking results are more responsive to changes in the weights assigned to detection perception ability while being less sensitive to alterations in survivability weights. This discovery implies that detection perception ability plays a crucial role in determining the performance ranking of earthquake search and rescue robots, aligning with our expectations as these robots need exceptional perception abilities to swiftly identify and locate trapped individuals within complex environments. Therefore, when formulating performance evaluation criteria for such robots, criteria related to detection perception ability should be thoroughly considered as they significantly enhance actual rescue effectiveness. Simultaneously, it is important to strike a balance among various criterion weights and avoid overemphasizing one aspect at the expense of neglecting other vital performances; this approach ensures better fulfillment of earthquake search and rescue robot requirements during real-life scenarios.
## 4 Discussion
### Mechanical Properties of the Root System  
When the root system of plants is subjected to mechanical stress, the stability of plants can be improved by increasing the tensile strength of upslope roots, the number and diameter of embedded secondary lateral roots [65; 66; 67; 68]. Therefore, many plant root systems have stronger upslope roots than downslope ones [69]. Wind loading, soil self-loading, and plant self-loading are all examples of standard mechanical stimuli. Numerous studies have demonstrated that the tensile strength of lateral roots is larger on high and medium slopes than on low slopes, and the tensile strength of root is likewise greater on uphill slopes than on downward slopes [65]. To avoid downhill displacement of the stump, plants growing on steep slopes have more robust root systems upward [70], while their root systems deform at a larger angle than those growing on flat surfaces [69; 71]. This phenomenon is explained by the fact that steep slopes exert a high degree of mechanical stimulation, and plants require greater anchoring to withstand this external stimulus. According to Figure 3C and Table 3, the quantity and tensile strength of embedded secondary roots are greater in the upslope than in the downslope, while the number of unembedded secondary roots is greater in the upslope. This conclusion is consistent with the findings of numerous other researchers [41; 67; 69; 71; 72]. This phenomenon could be related to self-loading plants. Due to shoot growth (self-loading increases) in plants growing on slopes, the upslope roots are asymmetrically distributed along the main roots, resulting in increased mechanical stress on the lateral roots [67; 69; 71]. Therefore, upslope lateral roots must increase in quantity, diameter, thickness, and tensile strength in order to withstand the pressure of external loads and, thus, improve plant anchoring. Additionally, numerous investigations have provided microscopic insight into the link between plant internal structure, chemical composition, and root mechanics [73; 74]. Tensile strength is proportional to the content of the root system's internal structural components, such as lignin, cellulose, and hemicellulose [75]. The root tensile strength was positively connected with the content of cellulose and hemicellulose and negatively correlated with the proportion of lignin [76]. Due to the extraordinary ability of cellulose in the root cell wall to preserve root tensile strength, cellulose plays a critical role in resisting root tension damage [74]. Increased water retention agent influences the water content of the root system, and this variation in water causes the cortical tissue to expand and contract, resulting in changes in the lignin to cellulose ratio and the area of the xylem fibers inside the root system [77; 78; 79]. [PERSON]'s anatomical results indicated that upslope lateral roots had a much bigger xylem fiber area than downslope lateral roots [67], implying that mechanical stress would result in an increase in xylem fibers [80]. The tensile strength of upslope roots
# Inorganic Waste Generated in Kraft Pulp Mills:
## 3 Main Properties of the Inorganic Wastes
### Potentially toxic metals  
To find reliable applications while protecting the environment, the total content of potentially toxic metals (PTM) and their leaching behavior are of high importance. Table 5 summarizes the total quantities of diverse PTM commonly found in the four inorganic wastes. For comparison purposes, three additional references are also included. In particular, the Finnish limits for the use of ash as a forest fertilizer, the Finnish limits in respect to the maximum allowable element concentrations in ashes (e.g., from coal, peat, and biomass) used as earth construction material [19] and the average crustal abundance of these elements. Table 5 reveals low concentrations for PTM in GLD, SG, LM, and BFA. The legal Limit FF is fulfilled for all elements, except for Ni in the GLD, whereas the Limit CM is exceeded only once for Zn (concentrations marked in bold in Table 5). The concentrations of PTM can also be compared to average crustal abundances and it is possible to conclude that some of the elements can be significantly enriched in these anthropogenic materials. Indeed, the concentrations of Pb, Cd, Cu, Cr, Ni, and Zn may be enriched in all wastes. However, it is possible to support the beneficial use of inorganic wastes instead of landfilling [19]. The discussion presented in the next section will demonstrate that among the possibilities of utilization, some of them encapsulate the waste in a matrix, and thus reduce the leaching processes. Indeed, from an environmental point of view, the leaching behavior of each PTM is more important than the total elemental content. It is well known that the leaching behavior of each element may present a specific pattern: amphoteric leaching (high leaching in acidic and basic pH conditions, with a U shape), cationic leaching (with high leaching in acidic pH), and a leaching pattern not dependent on the eluate pH. Moreover, the leaching can be controlled by solubility restrictions or availability. Thus, for each application, the leaching behavior must be considered to assess the environmental impact. In the literature, diverse studies addressed the leaching of these wastes. [PERSON] et al. [4] analyzed the leaching of GLD regarding Ca, Fe, K, Mg, Na, Si, Al, As, Cd, Co, Cr, Cu, Hg, Mn, Mo, Ni, Pb, Sn, and SO42-[4]. The leaching solutions of GLD revealed low Eh, high pH and EC, and thus low mobility of metals. The legal limits for inert landfills were exceeded only in a few samples for As, Cr, and Zn. [PERSON] et al. [19] highlighted that the very low metal concentrations in SG supports utilization instead of disposal in landfill, indicating that the concentrations of As, Ba, Cd, Cr, Cu, Mo, Pb, V, and Zn are clearly lower than the allowable concentrations in other materials used as an earth
# Study on Cold Resistance Performance of Composite Subgrade Structure in Seasonal Frozen Regions
###### Abstract  
In order to achieve the purpose of subgrade frost damage control and waste utilization, this paper proposes a specific kind of composite subgrade structure which is suitable for subgrade in seasonal frozen regions, especially wet subgrade. The composite subgrade structure is composed of extruded polystyrene (XPS) plates as a cold resistance layer and modified subgrade soil with excellent frost resistance which can consume a lot of oil shale residue and fly ash. To provide valuable reference for engineering applications, an outdoor model test is designed and carried out based on indoor test results and actual engineering data. The cold resistance performance of this new type subgrade structure is evaluated by comparing the temperature distribution, energy transfer, and freezing index of the composite subgrade and the common subgrade during the freezing process. The results show that, firstly, the cold resistance layer can effectively preserve temperature inside the subgrade and form a positive temperature zone beneath XPS plates, which can ensure that the subgrade soil in a certain range will not freeze during the freezing period. Secondly, the position with the best cold resistance effect of the cold resistance layer is directly under the XPS plate. In actual application, the key position should be covered as completely as possible by XPS plates to ensure the cold resistance effect. Thirdly, the cold resistance layer can not only protect subgrade soil under XPS plates from frost damage, but also raise and keep the service temperature of the structure above XPS plates in a certain range, which is beneficial to the cold resistance durability of the entire road. This means that the composite subgrade can greatly reduce the occurrence of subgrade frost damage, thereby even improving the service capability of roads in seasonal frozen regions.  
seasonal frozen regions; frost damage control; cold resistance layer; cold resistance durability; temperature distribution +
Footnote †: journal: Applied Sciences
# A New Period-Sequential Index Forecasting Algorithm for Time Series Data
## 2 Theoretical Model
### Error Evaluation  
In order to evaluate the obtained results, the forecasting accuracy was measured with three error indicators, that are the mean absolute percentage error (_MAPE_), the root mean squared error (_RMSE_) and the mean absolute error (_MAE_) [20,21]. Calculation equations of error indicators are given in the below Equations (25)-(27). In addition, the Pearson correlation coefficient (\(r\)) in Equation (28) is also used to quantify the strength and direction of the linear relationship between two sets of data during the reference historical period [22].  
\[MAPE=\frac{1}{N}{\sum}_{i=1}^{N}\left|\frac{y(t_{i})-F(t_{i})}{y(t_{i})} \right|\times 100\% \tag{25}\]  
\[RMSE=\sqrt{\frac{\sum_{i=1}^{N}\left(y(t_{i})-F(t_{i})\right)^{2}}{N}} \tag{26}\]  
\[MAE=\frac{\sum_{i=1}^{N}\left|y(t_{i})-F(t_{i})\right|}{N} \tag{27}\]* At the time step of \(t_{i}\), take \(Y\) and \(Z\) into the Equation (24), and solve it, and get the optimal weight matrix \(w\).
* Update the optimal values of \(c\), \(d\), \(e\), and \(f\) in Equation (10), solve Equation (10), and get the forecasting value \(F(t_{i})\).
* If the time steps of the stop condition (\(t_{i+1}>t_{N}\)) are satisfied, the search stops, and output parameters of _MAPE_, _RMSE_, _MAE_, and \(r\); Otherwise, the time step is added, and the procedure returns Step 2.
# A Technological Framework to Support Asthma Patient Adherence Using Pictograms
## 2 Materials and Methods
### Evaluation Process of Pictogram Comprehension by Users Diagnosed with Bronchial Asthma  
During this phase, we evaluated the comprehension level of the proposed pictograms among patients diagnosed with bronchial asthma. Users of the ARUs from health centers participating in the project were invited to participate, based on the following inclusion and exclusion criteria.  
Inclusion Criteria  
Adults aged 18-65 years that have been diagnosed with bronchial asthma.  
Have internet access and a cell phone, computer, or another electronic device.  
Provide an email address or telephone number for contact purposes.  
Manage or have support for the use of the Internet.  
Exclusion Criteria  
Presence of obstructive bronchial conditions other than bronchial asthma, such as chronic obstructive pulmonary disease (COPD) or mixed states.  
Presence of psychiatric pathology.  
Diagnosis of dementia.  
History of problematic alcohol consumption.  
History of substance abuse.  
The presence of significant ophthalmologic conditions that may hinder the proper visualization of images.  
Visual and/or hearing impairment.  
The sample size adhered to ISO 9186-1 guidelines, recommending a minimum of 50 participants for the evaluation of pictogram comprehension. These participants were distributed across the municipalities of Hualpen, Talachuano, San Antonio, and Valparaiso.  
For data collection, we utilized the instrument Questionnaire for Evaluating Pictogram Comprehension Among Patients with Bronchial Asthma, Users of Family Health Centers, Version 2.
# Advancing C-V2X for Level 5 Autonomous Driving from the Perspective of 3 GPP Standards
## 4 4G and NSA-Based 5G V2X Services
### Network Support  
The 3 GPP provides 4G & NSA-based 5G communication technologies-based network support to realize V2X services. The network support is two-fold: core network (CN), which deals with the core network and terminals (CT) support; and access network (AN), which deals with radio access network (RAN) support. In what follows next, we provide an important to-the-point overview of the core and access network support.
## 4 Recurrent Neural Networks (RNN)-Based Model
### Model Description
#### 4.2.2 Bi-LSTM Layer  
Unlike conventional standard LSTM models, which behave in a unidirectional way, _bidirectional LSTM_ (Bi-LSTM) models allow us to collect such information in both directions.  
A standard LSTM network takes as input the direct sequence of word embeddings and produces the outputs \(\vec{h}_{1},\vec{h}_{2},\ldots,\vec{h}_{N}\), where \(\vec{h}_{i}\) is the hidden state of the LSTM cell at time step \(i\), summarizing all the information that the network accumulated from our sentence up to word \(w_{i}\). Conversely, a Bi-LSTM consists of two LSTMs, a _forward_\(\overrightarrow{LSTM}\) that allows the analysis of the sentence from \(w_{1}\) to \(w_{N}\) and an _inverse or backward_\(\overrightarrow{LSTM}\) that allows a similar analysis to be carried out but in the opposite direction, from \(w_{N}\) to \(w_{1}\). The outputs of the Bi-LSTM layer are then obtained by simply concatenating for each word the outputs obtained from the analysis performed in each specific direction (as it can be deduced from Equation (1) where \(||\) corresponds to the concatenation operator and \(L\) to the size of each LSTM).  
\[h_{i}=\overrightarrow{h_{i}}||\overleftarrow{h_{i}}\,\text{ where }h_{i}\in R^{2L} \tag{1}\]  
\[e_{i}=g(h_{i}) \tag{2}\]  
\[a_{i}=exp(e_{i})/\sum_{j=1}^{N}exp(e_{j}) \tag{3}\]
## 4 Calculation of Various Reliability Indices
### Reliability of the WSN
#### 4.1.3 WSN Reliability with Fluctuation in Microcontroller Failure  
A microcontroller is an essential component of a wireless sensor network. To understand the reliability of the WSN with microcontroller failure, the author varied the failure rate of the microcontroller between 0.31, 0.32, 0.33, and 0.34 and kept the other failure rates fixed in the reliability expression obtained in Equation (20). The obtained results are graphically represented in Figure 5c.
# New Hybrid Techniques for Business Recommender Systems
## 5 Recommender Configurations
### CBR Recommender  
In the case of the CBR recommender, primarily three factors were considered in the configuration:  
* Similarity measures depending on attribute type: Based on the taxonomy-tree approach proposed by [54], the industry attribute uses the industry taxonomy derived by [3] that categorizes the customers of the consultancy based on their similarities (e.g., customers that are likely to share KPIs and dimensions). For the attributes goal (free text) and KPI, we could apply the TF-IDF [55] similarity measure by creating a corpus of goals and KPIs, respectively, from the case base for the computation of inverse document frequencies (IDFs). Although KPIs are not free text, applying TF-IDF is appropriate to disregard repeated terms such as \"Number of\" or \"Amount\" since they do not add significant value to the recommendations. Lastly, for the attribute target group, we calculated the Jaccard coefficient [55] as a case may have more than one target audience from the possible values \"employees\"/\"middle management\"/\"top management\".
* The number \(n\) of the most relevant (top) cases retrieved: The number of the retrieved cases played a significant role in calculating the scores of the recommended elements, which in turn determine the ranking. For an element appearing in any of the retrieved cases \(R(Q_{C})\) for a query \(Q_{C}\), the score of that element is the sum of the scores of all the retrieved cases in which the element occurs: \[score(i)=\sum_{C_{j}\in R(Q_{C}):i\in C_{j}}sim(C,C_{j})\] (1)Obviously, the larger the case base, the larger we can choose \(n\), i.e., the maximum size of \(R(Q_{C})\). For a rather small case base like ours, we expect that smaller values of \(n\) will work better since larger values will likely imply a \"topic drift\" by including rather dissimilar cases. The score of the case \(sim(C,C_{j})\) was generated by the CBR recommender using the global similarity function, which is the weighted average of the local similarity measures [56]: \(sim(C,C_{j})=\sum w_{k}sim_{k}(C,C_{j})\).
* For that weighted average, we used the weights assigned to the local similarity measures \(sim_{k}\) shown in Table 1.  
The retrieved ranking of matching cases was first filtered by business process such as to return only cases with matching process before applying the local similarity measures.
## 5 Experimental Section
### Impact Analysis for the ADG Scheme
#### 5.4.1 Threshold for Proxy Node Selection  
Proxy nodes are selected according to nodes' \(weight\). According to Equation (8), a node is promoted to be a proxy node if the \(weight\) is larger than a threshold \(th\). As showed at Figure 11, both the number of total messages transmissions and data coverage goes up to the maximal when \(th\) is around 0.4, and then goes down as the threshold increases. When \(th\) is small, e.g., 0.1, most the nodes are promoted as proxy nodes, so they just keep the data and avoid lots of message forwarding; yet \(MC\) could not visit all the proxy nodes at the epochs, leaving some proxy nodes unvisited and some data un-gathered by \(MC\), so the data coverage is low. When \(th\) is large, e.g., 0.6, fewer proxy nodes are selected, so there are fewer contact chances to upload its data to proxy nodes. This also leads to some un-gathered data at the ordinary nodes and makes the data coverage low. However, at this case the ordinary nodes would still try to forward their messages to the encountered nodes in order to be stored at the proxy nodes. So the number of total message transmissions is also high; more than 1.55E+5 messages are transmitted when \(th\) is more than 0.6. AD
## 4 System Model  
Consider a D2D communication underlaying LTE-A uplink cellular network, where _D = [1,2,.....,M]_ D2D users and _C = [1,2,...., N]_ cellular users are uniformly distributed in a cell as shown in Figure 1. A D2D transmitter and its predefined receiver form a D2D pair. For simplicity we assumed a single cell scenario with eNB located at the center of the cell. The probability density function (PDF) of a D2D pair is defined as:  
\[f_{i}(l)=\ \frac{2l}{(R_{i})^{2}},0<l\leq R_{i}. \tag{1}\]  
The complementary cumulative distribution function (CDF) is expressed by:  
\[F_{i}(l)=\ \int\limits_{l}^{R_{i}}f_{i}(l)dl=\ 1-\left(\frac{l}{R_{i}}\right)^{2} \tag{2}\]  
where \(R_{i}\) is the D2D communication range.  
As shown in the Figure 1, eNB is equipped with three 120\({}^{\circ}\)-directional antennas and assumed that the density of cellular users is greater than that of the D2D users in a cell. There are two main types of uplink interference generated due to D2D communications. In Figure 1a, signal transmitted from cellular user causes interference to D2D-Rx, and Figure 1b shows the interference generated by D2D-Tx to eNB. To mitigate the interference, we consider cell sectorization scheme in which cell area is divided into two non-overlapping regions, namely: inner and edge region. Cellular users and D2D pairs existing in inner cell region are named as \(N_{in}\) and \(M_{in}\), respectively. Similarly, cellular users and D2D pairs existing in edge cell region are named as \(N_{edge}\) and \(M_{edge}\), respectively. The locations of the both users are modeled based on the distance of the users from the eNB. Furthermore, each region is subdivided into three equivalent sub-sections. Therefore, the entire frequency spectrum is divided into six sub-bands and different sub-bands are allotted to each section of the cell. It is important to note that there is no interference among cellular users since the available resources are orthogonally assigned to all the cellular users by the eNB.  
For general realization, the received signal power from the eNB at the cellular user and D2D receiver is expressed as:  
\[P_{rec}=P_{T}d^{-\alpha}|h|^{2} \tag{3}\]where \(P_{T}\) is the transmit power, \(d\) is the distance between transmitter and receiver, and \(h\) is the channel coefficient.  
Therefore, the received signal of a typical user is given by:  
\[r(t)=r_{A1}(t)+\sum\limits_{ne\
eq A1}r_{ne}(t)+x(t) \tag{4}\]  
where \(r_{A1}(t)\) is the received signal from the desired user in a section of the cell, \(r_{ne}(t)\) is the sum of signals received from neighboring sections of the cell, and \(x(t)\) is the noise with power spectral density \(P_{N}\).
# Quasi-Yagi Slotted Array Antenna with Fan-Beam Characteristics for 28 GHz 5G Mobile Terminals
## 3 Experimental Results
Figure 19: Simulated and measured performances of the \(1\times 8\) array antenna: (**a**) simulated and measured reflection coefficients with the connector, (**b**) bending of the substrate, (**c**) radiation patterns at 28 GHz with a \(0^{\circ}\) beam steering angle, and (**d**) radiation patterns at 28 GHz with a fixed beam steering angle. Table 1 demonstrates the comparison on the performance of the proposed quasi-Yagi slotted array antenna and other state-of-the-art fan-beam antenna for mm-wave 5G applications. As indicated, the proposed antenna has the merit of a wider beamwidth.