esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Background: Small-field dosimetry remains an open challenge globally. Thus, it is crucial to consider adequate reference codes of practice for the performance of dosimetry. Furthermore, as part of good clinical practice, the implementation of new codes of practice implies the development of a dosimetry audit program. In this work, a pilot dosimetric audit protocol is established for measuring the absolute dose in water for small fields using micro-TLDs LiF:Mg,Ti dosimeters. Methods: The dosimeters were irradiated with a 6 MV X-ray beam in a linear accelerator. The TLDs were calibrated between 0.5 and 3 Gy for different field sizes. For audit, the TLDs were irradiated at 2 Gy for different circular field sizes. The proposed protocol consists of five TLD dosimeters forming a cross with a marked radiochromic film to identify the position of the central dosimeter during irradiation. Only the dosimeter measurement in the center of the field is used. Results: It was found that the percentage difference between the measured dose and the prescribed dose (2 Gy) for irradiation in circular fields is less than 3%. Conclusions: A pilot dosimetric audit was carried out using the proposed protocol over a linear accelerator using small circular collimator photon beams.  
micro TLD; radiotherapy; small photon beam; audit; absorbed dose Article  
Determination of the Absorbed Dose in Water for Small Photon Beams Using Micro-TLDs of LiF:Mg,Ti (Pilot Audit Proposal)
# Performance-Based Evaluation of Courtyard Design in China's Cold-Winter Hot-Summer Climate Regions
## 1 Introduction
[PERSON] [29; 30] and [PERSON] [31] researched the ecological strategy of traditional dwellings (e.g., simulating indoor thermal environments) in traditional civil dwellings during summer in the Anhui Province and a study of the wind movement in traditional quadrangle courtyards. In addition, [PERSON] et al. [32; 33] studied passive cooling techniques in traditional civil dwellings and conducted qualitative and quantitative evaluations of the wind and thermal environment in traditional civil dwellings, using ecological simulation software. [PERSON] also conducted a simulation-based analysis of a courtyard in Beijing, analyzing courtyard surface temperature distribution, to develop effective methods for mitigating or managing the micro-heat-island effect [34]. [PERSON] et al. [35] evaluated the thermal effects of internal courtyards in a tropical terrace house by computational simulation. [PERSON] et al. also provide some essential and detailed research on thermal comfort conditions, which considered radiant building surfaces and Predicted Mean Vote (PMV) assessment in living or working environments [36; 37; 38]. Current studies generally focus on filed measurements and simulation-based analysis for existing courtyards by considering passive ventilation, thermal comfort, and energy saving. Previous studies of the ecological effects of courtyards use the simulation analysis of traditional Chinese courtyards and lack sufficient consideration on how to build a desirable micro-climate environment for newly planned and designed courtyard buildings. More insight and guidance are needed to regulate and control different factors that influence the thermo-environment of courtyards in the design phase [39; 40; 41]. This study uses the design phase of Jiangnan Museum as a case study. According to the original design parameters, this study proposes an improved design considering the courtyard layout, the aspect ratio, and the ecological buffer area. The PHOENICS software (CHAM Company, Wimbledon, London, UK) for wind environment simulation, and the EnergyPlus-based software (Department of Energy's (DOE) Building Technologies Office (BTO) support, Berkeley, CA, USA), DesignBuilder (DesignBuilder Software Ltd., Gloucestershire, UK), for energy consumption simulation are employed based on the physical parameters of courtyards. This study:  
1. Proposes courtyard design strategies in terms of layout, aspect ratio, and ecological buffer areas in China's cold-winter hot-summer climate region;
2. evaluates the impact of the proposed design on the outdoor and indoor ventilation, thermal comfort, and energy efficiency of courtyards; and
3. provides insights for courtyard design considering local climate, courtyard geometry, and micro-ecological impact.
# Low-Complexity Channel Codes for Reliable Molecular Communication via Diffusion
## 2 Materials and Methods  
### Diffusion-Based Channel  
Let us consider a diffusion-based MC system operating in a three-dimensional space. The model assumed for the average number of molecules counted at the receiver for this work is the point transmitter to a spherical absorbing receiver [26; 27; 28; 29]. This type of receiver counts the number of absorbed molecules during an observation window \([t_{l},t_{u}]\). The probability, \(F_{hit}(t_{u},t_{l})\), of a molecule emitted at time \(t=0\) to be observed at the receiver during interval \([t_{l},t_{u}]\), can be defined as [26]  
\[F_{hit}(t_{u},t_{l})=\frac{a_{rx}}{d_{0}}\left[erfc\left(\frac{d_{0}-a_{rx}}{ \sqrt{4 Dt_{u}}}\right)-erfc\left(\frac{d_{0}-a_{rx}}{\sqrt{4 Dt_{l}}}\right)\right] \tag{1}\]  
where erfc is the complementary error function, \(a_{rx}\) is the radius of the spherical receiver and \(d_{0}\) is the distance between the center of the transmitter and the center of the receiver. \(D\) is the diffusion coefficient of the _iith_ molecule, m\({}^{2}\)s\({}^{-1}\), which can be calculated using  
\[D=\frac{K_{B}T}{6\pi\eta R_{p}} \tag{2}\]  
where \(K_{B}=1.38\times 10^{-23}\) is the constant of Boltzmann, \(T\) is the temperature in Kelvin, \(\eta\) is the fluid viscosity, and \(R_{p}\) is the radius of the particle.  
The approach taken in this work assumes a diffusion-based MC system whose information bits are encoded in order to reduce ISI. The concentration vector corrupted by noise at the \(kth\) time interval can be written as  
\[y[k]=\sum_{l=0}^{L-1}h[l,k]x[k-l]+n[k] \tag{3}\]  
where \(L\) is the assumed length of the ISI, \(x[k]\in\{A_{0},A_{1}\}\) (with \(A_{0}\) and \(A_{1}\) representing the levels for a '0' and a '1') denotes a concentration based binary modulation symbol emitted from the transmitter at time \(k\), \(h[l,k]\) is the number of molecules observed by the absorbing receiver during time interval \(k\) after \(N_{mocc}\) are released from the transmitter at time \(k-l\) and \(n[k]\) is the number of noise molecules detected by the receiver at time \(k\). Both \(h[l,k]\) and \(n[k]\) are modeled as Poisson random variables: \(h[l,k]\)\(\sim\)\(Poisson(\overline{h}[l,k])\) and \(n[k]\)\(\sim\)\(Poisson(\overline{n})\). Taking into account the probability model defined in (1), we have  
\[\overline{h}[l,k]=N_{mocc}\).\(F_{hit}((l+1)T_{s},l.T_{s}). \tag{4}\]  
It can be shown that the received concentration at the receiver can be expressed as  
\[y[k]=\underbrace{\overline{h}[0,k]x[k]}_{\text{desired signal}}+\underbrace{v[ k]}_{\text{diffusion noise}}+\underbrace{I[k]}_{\text{ISI}}+\underbrace{n[k]}_{\text{ environmental noise}} \tag{5}\]  
where the diffusion noise follows the distribution \(v[k]\)\(\sim\)\(Poisson(\overline{h}[0,k]x[k])\), i.e., it is equivalent to a Poisson random variable whose mean has been subtracted. This noise is signal dependent and accounts for the deviation of the concentration from its expected value according to the diffusion model, as illustrated in Figure 1.
# Elimination Properties for a Probabilistic Scheduling Problem
## 7 Computational Experiments
### Algorithm Stability
#### 7.2.1 Algorithms' Efficiency  
Computations of probabilistic algorithm \(TS_{\mathcal{P}}\) and its version with blocks \(TS_{\mathcal{P}+\mathcal{B}}\) were made on the examples from set \(\mathcal{D}\) (see Section 7). The received results were compared with the best currently known. For each example, the relative error \(\delta\), as well as calculation time, were calculated. Average relative deviation values \(\delta_{aprd}\), for individual groups of examples, are shown in Table 1. The computations time for a run was fixed at 60 s in each instance.  
As expected for all groups of examples, the average relative error is smaller for the algorithm with blocks. The difference is particularly noticeable for large-scale examples. For \(n=100\) tasks, the relative error of the algorithm without blocks is 59.84%, and with blocks only 6.65%, for the same calculation time of one minute for a single test instance.
## 1 Introduction
### Knowledge Gaps  
In recent years, the application of extended reality, encompassing virtual reality, augmented reality, and mixed reality (MR), has gained increasing attention in the construction sector, particularly in relation to safety management. Numerous studies have demonstrated the benefits of XR technologies for worker training, hazard visualization, equipment identification, and simulated task performance. However, despite the growing interest and the development of isolated XR applications, the field remains fragmented. Most contributions focus on specific use cases or disconnected technological prototypes rather than on integrated systems that support safety holistically across the lifecycle of construction projects [33].  
A central shortcoming in the current body of research is the lack of holistic and standardised frameworks that effectively embed XR technologies into comprehensive safety management processes [34]. While certain studies propose frameworks or workflows for XR deployment, these often fail to incorporate the broader methodological foundations needed for real-world implementation, such as integration with Building Information Modelling (BIM), Lean Construction principles, and Construction 4.0 environments. Moreover, there is a deficit in lifecycle thinking: XR is rarely used from the early design stages through to construction, operation, and monitoring. This limits the technology's capacity to become a truly transformative tool for accident prevention [35; 36].  
In addition, few existing approaches explicitly address the multifactorial nature of construction safety. Current XR solutions tend to emphasize technical risks or spatial hazards while overlooking the integration of contextual factors such as environmental conditions, organizational culture, work dynamics, and human behaviour [37]. The lack of customisable and emotionally engaging XR experiences--which would align simulations with workers' specific roles, experiences, and cognitive responses--represents another barrier to achieving deeper awareness and behavioural change. Although storytelling and gamification have shown promise in other sectors, their strategic use in construction safety remains underexplored and insufficiently justified [38].  
Another significant gap concerns the technical-operational readiness and scalability of XR-based safety systems. Many studies stop at the conceptual or pilot level, without detailing the architecture required for implementation or the technical processes for data integration, interface design, and real-time updates. This creates a disconnect between academic innovation and industry adoption [39]. Moreover, the lack of standard validation procedures, benchmarking, and comparisons with traditional safety tools hinders the ability to assess XR's real effectiveness. There is also limited reference to existing international standards, such as ISO 19650 Part 6 [40], which could guide the structured inclusion of safety data in digital environments [41].
# Highly Integrated Wideband Transmit/Receive Module for X-Band SAR Applications
## 3 Radio Frequency Design
### Directional Coupler Design  
The directional coupler is necessary to couple the SCFE output signal from/to the calibration port. It was designed to have a coupling factor of about \(-30\) dB and a directivity better than 15 dB. The design principle was quite simple, involving a trace close by the SCFE output microstrip, although a fine optimization was necessary to reach the desired values. The initial structure and the port numbering for evaluating the coupler performances are shown in Figure 4. The coupled line is buried in the second stack-up layer to minimize the risk of corona discharge, since the latter is attenuated by the dielectric interposed between the SCFE output microstrip (on the top layer) and the coupled line on the second layer (embedded microstrip type of interconnect). The unneeded port of the directional coupler is loaded by a \(50\)\(\Omega\) termination connected to the ground layer. The \(50\)\(\Omega\) resistor is directly printed on top of the substrate, and on the top layer, there are conductive pads using a conductive material with \(100\)\(\Omega/\square\) resistance. The final design parameters are shown in Figure 5.
## 4 Experiments  
For the experiments, we implemented the proposed deep learning model alongside several baseline classical machine learning models, including Decision Tree (DT), K-Nearest Neighbors (KNNs), Random Forest (RF), Logistic Regression (LR), Adaptive Boosting (AdaBoost), Gradient Boosting (GB), Extreme Gradient Boosting (XGBoost), and Categorical Boosting (CatBoost). Additionally, we employed deep learning models including Convolutional Neural Network (CNN), Multi-Layer Perceptron (MLP), Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), Bidirectional LSTM (Bi-LSTM), and Bidirectional GRU (Bi-GRU). This comprehensive approach facilitates an efficient comparison and provides a clearer demonstration of the proposed model's performance. To familiarize readers with the structures and capabilities of these baseline models, we also provide concise explanations of some recent models.
## 3 Results and Discussion
### E-Field and H-Field Distributions  
The absorption mechanism can also be understood through (Magnetic field) H-field and (Electric field) E-field analysis [60]. The inter-relationship of these features can be assumed through the Maxwell equation [61; 62; 63]. The E-field is resonantly confined at a particular portion of the symmetrical structure. Figure 11 shows the E-field and H-field at in TE mode, where at 12.62 GHz frequency E-field is highly confined at the upper side of the external ring, and the strong H-field appears at the four corners of the outer ring. The intense magnetic field achieves absorption peaks at 14.12 GHz contributed by the vertical bar of the inner Jerusalem cross. On the other side, the E-field is strong in the left and right  
Figure 10: Absorption plot of different polarization angles in (**a**) TE and (**b**) TM mode and absorption plot of different oblique angles in (**c**) TE and (**d**) TM mode.  
Figure 9: Wave vector direction in (**a**) TE and (**b**) TM mode.  
portions of the outer ring. The near-unity absorption at 17.53 GHz is contributed by the strong H-field of the right and left sides of the inner and outer ring, where less intensity appears in the E-field. The microstrip line on the outer ring's external side influence the absorption peaks at 19.91 GHz. The two opposite sides of the microstrip line have an intense E-field, and the center shows high H-field intensity. Figure 12 shows the H-field and E-filed allocations in TM mode, where field intensity is similar to that in TM mode but rotated at 90 degrees.
# Safe and Sustainable Design of Composite Smart Poles for Wireless Technologies
## 2 Materials and Methods
### Computational Fluid Dynamics  
The design power range of 100-500 W, as the operating power, required by the 5G radios alone, can be categorized as high-power electronics (HPE). For uniform spatial coverage, multiple directional radios are required leading to \(\approx\)1 kW power consumption. The power range is of the same order as the graphics processing units (GPUs) for which liquid cooling systems are commonly employed to maintain moderate temperature levels at components. Hence, one of the ultimate needs of the pole thermal management is the creation of an efficient cooling concept for its HPE. As a common concept for current 5 GPs, the initial design was based on air cooling.  
For the CFD investigations, the Reynolds Averaged Navier Stokes (RANS) and Large-Eddy Simulations (LES) were utilized [9; 33]. In the air cooling simulations, the target was to solve the Navier-Stokes equations along with a transport equation for temperature using the standard, incompressible pimpleFoam solver of the open source CFD code OpenFOAM. The liquid cooling heat transfer simulations were performed with CHT analysis using the standard chtMultiRegionFoam solver in OpenFOAM were the conservation of mass, momentum and energy are calculated simultaneously in both, the liquid and the solid domains. For the heat management, the cooling of four radio units was analyzed. Appropriate cooling capacity requires proper heat exchange and design with optimized internal channeling either inside pole shaft (air cooling) or liquid cooling. Also, a strategy for connecting the cooling medium flow through the four heat exchangers is needed. The 3D printed aluminium heat exchanger was tested for a version family 'V1'. Due to weight, a version family 'V2' and water-cooled heat exchangers were analyzed with the following objectives: (1) minimum material costs, (2) low surface temperatures, (3) more compact size than V1 versions, and (4) the system should be functional even if lukewarm water is available. The simulation parameters are given in Table 2. The design concept with flow and heat values are given in Figure 7. The model consisted of 11 million cells in the fluid domain and three million cells in the solid domain--hexagonal cells except at the edges/interfaces cubic cells were used.  
Figure 7: The cooling design for an integral device configuration in the smart pole(s): the estimated input data for thermal analysis of a heat exchanger by using CFD.
# Ecosystem Water Use Efficiency in the Three-North Region of China Based on Long-Term Satellite Data
## 2 Data and Methods
### Analytical Methods
#### 2.3.6 Impacts of Climate Factors and Human Activities  
In this study, the multiple regression residual method was used to separate human activities and climate factors by assuming that WUE had linear correlations with the combinations of precipitation and temperature, and the residual of such correlation could be regarded as the impact of human activities:  
\[\text{WUE}_{\text{Climate}}=a\times P+b\times T+c\text{,WUE}_{\text{Human}}= \text{WUE}-\text{WUE}_{\text{Climate}}, \tag{16}\]  
where \(\text{WUE}_{\text{Climate}}\) is the contribution of climate factors, and \(P\) and \(T\) are the precipitation and temperature, respectively. \(a\), \(b\), and \(c\) are regression coefficients, and \(\text{WUE}_{\text{Human}}\) is the contribution of human activities. To evaluate the impacts of climate factors and human activities, the Sen slopes of \(\text{WUE}_{\text{Climate}}\) and \(\text{WUE}_{\text{Human}}\) are calculated to divide their impact into seven grades, as shown in Table 1: significant inhibition, moderate inhibition, slight inhibition, almost no effect, slight promotion, moderate promotion, and moderate promotion.
## 2 Processing of HS-SPME-GC-MS Data on Wine Volatiles by Multivariate Statistics  
Multivariate statistical methods are used in different ways to process HS-SPME-GC-MS data on wine volatiles. The main approaches are: (i) hypothesis testing, to determine, for example, whether there are statistically significant differences between different datasets obtained under different experimental conditions or treatments [40; 41; 42; 43]; (ii) exploratory analysis, through which the main similarities and dissimilarities between multivariate data are highlighted [4]; (iii) regression models, by which values of the dependent variables are predicted from those of the measured (independent) variables [23; 24]; (iv) pattern recognition, aiming to create clusters of similar multivariate data (unsupervised methods/clustering) or to identify relationships between different variables that allow each of the available data to be classified in a class known a priori (supervised methods/classification) [4; 44]. Both unsupervised and supervised methods also make it possible to associate an unknown data item with one of the identified clusters or to classify it in one of the a priori known classes. With regard to supervised classification methods, these include a training step, during which a model is optimized for the best possible classification of a multivariate dataset after a prior classification is given for samples. The application of these methods also requires an accurate validation, consisting in evaluating the model performance on further samples, not used for training, whose classification is known a priori. Once validation is completed, the obtained model can be exploited to classify samples whose classification is unknown, starting from multivariate data. In some cases, besides allowing the classification of new samples, it is also possible to study the factors (e.g., the combinations of independent variables) that the method itself identifies as the basis for the best classification.  
In some cases, the same multivariate statistical technique can be used for different objectives. For example, analysis of variance techniques (ANOVA-MANOVA) can be used both as hypothesis tests and, by means of various post-hoc methods, to obtain data clustering. Other methods, such as PCA, are used for exploratory analysis, for clustering and to determine latent factors. Other methods, such as PLS or Artificial Neural Networks (ANNs), can be used for both regression and pattern recognition (Figure 2).  
The following paragraphs describe the main multivariate statistical methods used for the analysis of HS-SPME-GC-MS data on wine volatiles. For each method, a brief description of its principle of operation, the peculiar characteristics, and the different applications is given.
# A Jamming-Resilient and Scalable Broadcasting Algorithm for Multiple Access Channel Networks
## 2 Related Works
The anti-jamming method considers the angle between the jammer and the sender channel's vector. The simulation results showed improvements in the peak signal-to-noise ratio. [PERSON] and [PERSON] [1] studied the ability of WLANs to withstand network attacks. For the IEEE 802.11 and IEEE802.11b packets, garbling a single bit of a packet will render the packet useless. They proposed an anti-jamming technique using both cryptographicallystrong interleavers and error-correction codes. This anti-jamming method can maintain a good throughput under moderate jamming when using LDPC codes. Next, we present an overview of broadcast algorithms for MAC networks. The broadcast algorithm design can be classified into three main types: ALOHA, splitting algorithms, and window-based algorithms. The first broadcast algorithm, known as ALOHA, was developed in the 1980s [35]. It works by having stations reatempt transmission of collided messages with a constant probability. Slotted ALOHA was developed, which improves the maximum throughput by partitioning time slots [36]. The station can only transmit messages at the beginning of a time slot. Tree-based algorithms were developed individually and independently by [PERSON] [37], [PERSON] [38], and [PERSON] [39]. They implemented a broadcast algorithm based on the idea of a binary search tree. In this algorithm, only nodes that were involved in collision participate in the collision resolution. It works by splitting the set of nodes into two halves by flipping a coin. This process continues until a set with a single node is found. [PERSON] and [PERSON] developed several broadcast algorithms for MAC networks, including Counting Backoff, Queue Backoff, and Quadruple Round [19]. These algorithms are deterministic and do not use any randomization. The authors studied them using an adversarial injection model with a restriction on the number of stations that can be activated. They showed that the algorithms could attain bounded packet latency for different injection rates.