esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Overview of the Investigation Approach  
In the present LCA study, different road subbase alternatives with relevance to availability in Vietnam were considered--namely, manufactured sand (m-sand), two types of steel slag (granulated blast furnace slag (GBF) and electric arc furnace slag (EAF)), construction and demolition waste (CDW), and fly ash (FA). Using LCA, the environmental footprints of the substitutive building materials were compared with one another and with the corresponding conventional layers made from primary materials (m-sand). The study comprised the following working steps:  
1. A comprehensive review of the respective materials (as documented in the introduction/literature review).
2. Chemical and soil mechanical analysis of secondary road subbase alternatives.
3. LCA of the material alternatives in the context of the Vietnamese road construction sector.  
The material alternatives can be characterized as follows (illustration in Figure 1):  
**Manufactured sand (m-sand):** Crushed primary rock material containing processed aggregates smaller than river sand's grain size using confined process machines  
**Granulated blast furnace slag (GBF):** Blast furnace slag is a non-metallic byproduct formed in molten conditions along with molten iron during the processing of iron ore, along with coke and limestone. Blast furnace slag consists of silica, alumina, and lime, combined with magnesia, sulfur, and oxides such as iron oxide and manganese oxide [47; 48]. Based  
Figure 1: Impression of the considered SBMs (from left): GBF, EAF, CDW, and FA (Figure source: [PERSON] and [PERSON]).  
on molten slag cooling, there are three types of blast furnace slag: granulated, air-cooled, and expanded/pelletized [49]. GBF is formed by the rapid cooling of slag by water jets and ground to produce sand-like granules, widely used in cement production.  
**Electric arc furnace slag (EAF):** Obtained in steel production when steel scrap is remelted. After defined cooling, metallurgical tool slag is similar to natural volcanic lava.  
**Construction and demolition waste (CDW):** Mineral waste such as walls, roof tiles, tiles, rubble, or sand that accumulates during construction measures such as the renovation, gutting, or demolition of buildings.  
**Fly ash (FA):** Fine, powdery material composed mostly of silica made from the burning of finely ground coal in a boiler.
# Influence of Internal Pressure on Hollow Section Steel Members in Fire
## 2 Case Study  
A fire broke out on 23 May 2023, in Tallinn, Estonia. The fire originated in the open-air storage area of a waste plant and quickly escalated. Within a few hours, it had spread to the storage facility of a nearby manufacturer of packaging products and packing machinery. This incident had a significant impact on the entire city of Tallinn. This facility, constructed in 2021, is relatively new; one of the authors of this publication has consulted the fire resistance issues of the design. The structural design of the storage facility follows a common regional approach and can be summarized as follows:  
* Reinforced concrete columns (height ~12.0 m);
* Steel trusses (span 24.0 m) support the lightweight roof;
* Sandwich panels were used for the exterior walls;
* The total area of the storage is ~11,000 m\({}^{2}\).  
The fire did not extend further into the building, despite the high fire load density inside the storage, reaching up to 10,000 MJ/m\({}^{2}\). This suggests that the structure and its associated systems exhibited better resistance than originally designed for. The seriously damaged area of the facility was around 1000 m\({}^{2}\).  
A view of the IFC model of the main steel truss as designed is presented in Figure 1. The steel trusses were composed of cold-formed square hollow sections. The upper profile of the truss was originally designed as a CFSHS 300 \(\times\) 300 \(\times\) 8 section. The damaged structure offered an unexpected sight. In Figure 2, photos of the collapsed main trusses are shown. It is evident that the shape of the upper chord cross section transformed from original square to an elliptical form. Similar deformations were noticed in many other tubular members of several trusses. No evidence of steel profile fractures or weld failures was observed.  
The cause of this deformation most obviously was the increase in internal pressure as the temperature of the gas confined within the profile rose. This paper addresses theoretically the possible effects of the internal pressure on the load-carrying capacity of steel hollow section profiles.
## 2 Objectives  
The main objective of this proposed Special Issue is to collect contributions from active researchers in the field of hysteresis and from structural, electrical, materials, and other engineering fields. It will act as a platform for sharing, for instance, the latest developments in this field, including new modeling techniques, or the system identification of complex hysteresis models. Given the interdisciplinary nature of this topic, the proposed Issue will be a collection of contributions from scholars in several fields and will cover topics such as: Nonlinear phenomena in hysteretic systems [1]; Hysteresis in the study of magnetic fields [2]; Analytical models for predicting and analyzing hysteretic behavior [3]; The role of hysteretic restoring force on modal interactions [4]; Hysteresis in mechanical systems modeling and dynamic response [5; 6; 7]; Hysteresis modeling applications in electrical engineering [8]; Advances in hysteresis modelling [9]; The use of smart materials in the modelling of hysteresis systems [10]; Hysteresis and its measurement [11]; Artificial-intelligence-based methods for modeling hysteresis [12; 13]; System identification of hysteretic systems [14; 15; 16; 17]; Hysteresis in seismic analysis [18; 19]; Study of the Bouc-Wen-Baber-Noori model and its applications [20]; the Prandtl-Ishlinski model for modeling asymmetric hysteresis [1; 13]; Hysteresis in control systems [18; 19]; and Random vibration of hysteretic systems [21].  
Hysteresis in a system is often the macroscopic effect of complex phenomena taking place on a smaller scale. While studying the causes of hysteresis requires microscale experiments and models, hysteresis itself can often be studied directly at the system scale. It is therefore customary for researchers in fields where hysteresis is observed to engage in  
Figure 1: Hysteresis loop of different systems modeling: (**a**) Mechanical damping system; (**b**) Electrical system; (**c**) Magnetic system.  
modeling and experiments at both scales. Whereas the study of hysteresis in the field of magnetic materials is well established and can be considered a standard subject, this is not the case in the field of mechanics, where hysteresis is also relevant.
# Impact and Stability of mandibular Setback after Intraoral Vertical Ramus Osteotomy
## 2 Materials and Methods  
### Study Design and Patients  
A retrospective cohort study was undertaken using patients' files from the database of the Oral and Maxillofacial Surgery Department at Sheba Medical Center between the years 2005 and 2021.  
Included were all patients diagnosed with mandibular prognathism and treated with IVRO surgery with subsequent IMF for 6 weeks. All included patients had documentation of clinical and radiological preoperative, intraoperative, and postoperative examinations. Postoperative follow-up of at least 8 months was necessary for inclusion in the study.  
The records of 39 patients were screened. Seventy-eight IVRO surgeries were included. It was noted that 46% of patients underwent a genioplasty procedure in addition to IVRO.  
Patients' charts were reviewed for data regarding demographics, clinical and radiological examination, including occlusion diagnosis, overbite and overjet, skeletal measurement, neurosensory deficit, as well as other symptoms such as sensitivity or noisiness in TMJ function, and maximal interincisal opening were taken at the following specific time points; preoperatively (T0), 3 days postoperatively (T1), and 8 months postoperatively (T2).
## 1 Introduction
This study used a mobile UV laboratory designed and commissioned for testing with large (\(\sim\)1 m\({}^{3}\)/s) airflows ([PERSON] et al., 2017). This study data obtained under simulated swine conditions with fast-moving airflows, like a real swine farm, is considered helpful in evaluating UV photocatalysis performance. Results from this study are needed to inform the experimental design for future on-farm research with UV-A and UV-C. Box 1 provides definitions of key acronyms used in this paper. **Box 1. ** Definitions of key acronyms used in this paper. **CH\({}_{4}\)**: methane  
**CO\({}_{2}\)**: carbon dioxide  
**DMDS**: dimethyl disulfide  
**DMTS**: dimethyl trisulfide  
**ECD**: electron capture detector  
**FID**: flame ionization detector  
**GC-MS**: gas chromatograph-mass spectrometer  
**GHGs**: greenhouse gases  
**H\({}_{2}\)S**: hydrogen sulfide  
**J**:oule (unit of energy)  
**LED**: light-emitting diode  
**NIST**: National Institute of Standards and Technology  
**NH\({}_{3}\)**: ammonia  
**N\({}_{2}\)O**: nitrous oxide  
**OU\({}_{E}\)**: odor unit  
**O\({}_{3}\)**: ozone  
**ppb**: part per billion  
**ppm**: part per million  
**SPME**: solid-phase microextraction  
**TiO\({}_{2}\)**: titanium dioxide  
**UV-A**: ultraviolet light (315-400 nm range)  
**UV-C**: ultraviolet light (100-280 nm range)  
**VOCs**: volatile organic compounds  
**W**: watt (unit of power)
## 2 Materials and Methods  
### The Study Area  
The study was conducted in Sawena district, Eastern Bale Zone, Oromia National Regional State, and Southeast Ethiopia, located 605 km and 60 km from Finfinne, the country's capital, and Gini, the zone's center, respectively (Figure 1). Micha Town serves as the district's capital and administrative center. The district borders the Somalia National Regional State to the east, Lega Hida district to the north, Gololcha district to the west, Ginir district to the south, and Rayitu district to the southeast. The district lies between the latitudes 60\({}^{\circ}\)59\({}^{\prime}\)40\({}^{\prime\prime}\) N-70\({}^{\circ}\)46\({}^{\prime}\)15\({}^{\prime\prime}\) N and longitudes 40\({}^{\circ}\)47\({}^{\prime}\)00\({}^{\prime\prime}\) E-41\({}^{\circ}\)42\({}^{\prime}\)15\({}^{\prime\prime}\) E. The district spans approximately 8263 km\({}^{2}\) and divides into 28 rural kebeles and one urban kebele [38].  
Physically, the district is characterized by sub-tropical highlands, lowlands, rugged areas, deep gorges, and flat-topped plateaus. The Sawena district's altitude increases to 2970 m above sea level. The district is classified into sub-tropical ('Woina dega') and tropical ('Kola') thermal zones. The district's average annual temperature varies from 21.5 to 38 \({}^{\circ}\)C, with an average annual rainfall of 350 to 700 mm [38].  
The population of Sawena district is estimated at approximately 98,902, with 93.29% residing in rural areas and 6.71% in urban areas. The district has an average population density of 10.71 people per square kilometer [38]. Sawena is home to diverse ethnic groups, with 90% of the population being Oromo and the remaining 10% comprising Amhara and Somali communities. Afan Oromo is the dominant language spoken across the district, along with several other languages. In terms of religion, 98% of the population is Muslim, while the remaining 2% follow Orthodox Christianity, Waqefata, Catholicism, and other religions [39].  
Figure 1: Map of the study area. Source: authors’ own construction.  
The primary livelihood systems in the study area are agro-pastoralism, which accounts for 24.13%, and livestock production, which makes up 75.77%. Land use classifications include crop production (2.13%), grazing land (25.17%), forest and bushland (6.44%), with other uses comprising 66.26%. The region's typical vegetation cover includes shrubland and bushland, largely dominated by various Acacia species with minimal or no herbaceous growth. The average landholding per household in agro-pastoralist areas is 2.08 hectares [40].
## 6 Discussion
### Design Implications  
The iterative expert consultation process generated several actionable insights for designing emotionally grounded and developmentally appropriate digital interventions for young children. Instead of concentrating on superficial aesthetics or basic usability concerns, experts emphasised the significance of relational framing, cognitive scaffolding, and autonomy support. These insights informed the following four design implications:  
1. Reframing Transitions as Empathetic Interactions Experts advised against the use of direct commands and externally imposed language--such as 'promises'--that may place emotional pressure on children or trigger feelings of guilt. Instead, transitions should be presented as relational appeals for assistance or empathy, leveraging on the natural inclination of children to anthropomorphic digital characters. This approach repositions disengagement not as a loss, but as a caring and emotionally meaningful act.
2. Supporting Time Awareness through Predictable Visual Cues Given the limited understanding of time and self-monitoring capacities of preschool-aged children, experts emphasised the need for persistent and intuitive visual indicators of session duration. In response, a colour-coded vertical timer was integrated and displayed continuously throughout the session to provide children with consistent temporal awareness, thereby supporting the development of early executive function.
3. Fostering Intrinsic Motivation through Autonomy Support Experts cautioned against the use of rigid performance metrics and binary success/failure framing, as these may diminish the sense of competence in children. Instead, they recommended providing descriptive feedback that acknowledges effort and incremental progress. They also recommended offering multiple closure options to reinforce autonomy, enabling children to select the modality--e.g., voice, gesture, and physical interaction--that best aligns with their individual preferences.
4. Adapting Modalities to Developmental Needs As preschool-aged children depend on multiple sensory channels for interaction--particularly given their still-developing literacy skills--experts recommend the integration of multimodal input and output mechanisms. These include tactile, verbal, and physical gesture-based interactions to ensure developmental appropriateness. The inclusion of embodied actions, such as placing the device into a house-shaped charger, was highlighted as a meaningful extension of this principle.  
Collectively, these design implications represent a shift from behaviour control to behaviour support, grounded in principles of developmental psychology. They highlight the importance of designing systems that scaffold cognitive and emotional capacities in children rather than constrain them. The feedback also reinforces the need for adaptive and responsive interfaces that accommodate the diversity of child temperaments and developmental trajectories.
## 3 Results and Discussions  
In this section, we present the performance analysis of the routing heuristics based on the four warehouse configurations and three paperless picking solutions. The cost inputs are determined based on a 2-year investment horizon and reflect the technological structure of each picking system. As indicated in Table 1, each warehouse has distinct characteristics. For example, warehouse layout 4 contains 20 aisles with 40 storage positions each, but pickers in that warehouse can travel 65 m per minute on average. This speed reflects the type of picking devices used in warehouse 4. Likewise, the picking cart capacity is 48. The capacity and speed are chosen to correspond with the  
Figure 1: Flowchart of the methodological framework.  
size of the warehouse. Warehouse 4 uses a larger cart to offset the long distance that a picker might need to travel per batch. The more order a picker can pick per batch, the smaller will be the average total distance. However, due to weight constraint and safety concerns, pickers in warehouse 4 travel at a lower speed than the middle size warehouses (2 and 3). We assume that picker blocking is negligible. Following [15], the cost components are derived as in Table 4. Our cost values are adjusted to reflect the warehouse size and new information obtained from the industry catalogue and the literature. The usage hours are calculated using an eight-hour work shift for 220 days a year. We used CPLEX 12.10 (with default options and 60 s time limit for each run) to solve the MILP models Ref. [24; 27] and calculate the cost efficiency values, Matlab R2020a to implement the method of [34], and Microsoft Office Professional Plus 2019 to compile the data generated. The software are run on an ASUSPRO with 3.00 GHz processor and 24 GB RAM with a Microsoft Windows 10 Enterprise operating system.  
As previously mentioned, the time required to process a batch varies with the paperless technology and the number of items. Likewise, the time needed to fix an error depends on the technology used. The three paperless solutions considered in our experiment are barcodes handheld, RFID tags handheld, and voice picking. Those three technologies are chosen because they allow order pickers to move freely within the warehouse.  
For the ranking and subset selection, the parameters are chosen as follows:  
- Probability of Correct Selection: \(P^{*}=0.95\).  
- Size of the subset: \(m=10\).  
- Number of replications in the first stage sampling: \(n=100\).  
- Indifferent amount: \(d^{*}=0.01\).
## 2 Materials and Methods
### Data Set Partitioning  
To divide the processed and labeled data into training, testing, and validation sets, we will use the DataLoader package in Python. Firstly, we will set the batch size to 32 and the random state to 42 to ensure consistent results each time. This will guarantee that the dataset is shuffled in a reproducible manner.  
Next, we will create three data loaders: the training data loader, the testing data loader, and the validation data loader. The data loaders will allow us to efficiently load and iterate through the data during model training and evaluation.  
The training data loader will be responsible for providing batches of data during the training process. It will randomly sample 70% of the data for training.  
The testing data loader will be used to evaluate the model's performance. It will contain 15% of the data and will be used to assess how well the model generalizes to unseen examples.  
The validation data loader will also contain 15% of the data and will be used to finetune the model's parameters and assess its performance on a separate dataset. This will help ensure that the model's weights are optimized and prevent overfitting to the training data.  
By using the DataLoader package and setting the appropriate parameters, we can create data loaders that provide randomized and rigorous training, testing, and validation data for our model.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Classification**} & \multirow{2}{*}{\
\begin{tabular}{c} **Aspiration** \\ **Type** \\ \end{tabular} } & \multicolumn{5}{c}{**CAE Equation Parameters**} \\ \cline{3-6}  & & \(A_{1}\) & \(A_{2}\) & \(B_{1}\) & \(B_{2}\) & \(D\) \\ \hline \multirow{3}{*}{
\begin{tabular}{c} Wheat \\ \end{tabular} } & Desorption & 4.212 & 4.796 & 7.493 & 4.028 & 202.031 \\  & Adsorption & 4.874 & 4.767 & 4.671 & 3.639 & 201.676 \\  & Desorption & 4.431 & 4.883 & 7.758 & 4.373 & 205.097 \\  & Adsorption & 4.606 & 4.561 & 4.918 & 3.613 & 202.632 \\  & Desorption & 4.393 & 4.845 & 7.843 & 3.858 & 203.892 \\  & Adsorption & 4.812 & 4.479 & 4.783 & 3.799 & 202.164 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Parameters of the CAE equation for the main grain types.
# Interference Alignment for Cognitive Radio Communications and Networks: A Survey
## 2 Interference Alignment
### Applications  
IA is a common element of most multiuser communication network capacity problems. Indeed, IA comprises of wide-ranging techniques to manage interference among users, and it can be implemented in various multiuser communications networks to perform interference management. Owing to its capable performance, IA has been applied to CR networks, heterogeneous networks, ad hoc networks, 5G cellular wireless networks, satellite networks, underwater networks, etc., as shown in Table 3. With the advance and growth of wireless communication networks, innovative classes of IA techniques will certainly materialise to solve the interference problems in the advanced wireless systems.  
In this paper, the focus will be on IA in CR networks. Our main objective is to provide a systematic overview on IA in CR communications and networking. We review the key system models/paradigms involved in the implementation of IA in CR networks and explain how these are crossly related.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**References**} & \multirow{2}{*}{**IA Techniques**} & \multicolumn{2}{c}{**Signal Dimensions**} \\  & & **CSI** & **(Space, Time, Frequency, Time-Frequency)** \\ \hline
[29; 30; 31; 32] & Linear IA & Perfect/delayed & Single/Multi \\
[33; 34; 35; 36] & Distributed IA & Local & Single \\
[37; 38; 39; 40] & Subspace IA & Perfect & Multi \\
[29; 41; 42; 43] & Blind IA & Absent & Single \\
[25; 44; 45] & Ergodic IA & Perfect/delayed & Single \\
[36; 46; 47] & Retrospective IA & Delayed & Single \\
[26; 39; 43] & Lattice Alignment & Perfect & Single \\
[40; 48; 49; 50; 51] & IA and Cancelation & Perfect & Single/Multi \\
[27; 52; 53] & Opportunistic IA & Perfect & Single/Multi \\
[28; 54; 55] & Asymptotic IA & Perfect & Single \\ \hline \hline \end{tabular}
\end{table}
Table 2: List of general IA techniques.