esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract
Several authors have demonstrated that people who migrated from middle- and low-income countries to high-income countries presented a lower oral health status, a condition due to cultural habits, religiosity, and social behaviours of their native nations [19; 20; 21]. [PERSON] et al. [22] recorded in his systematic review a significantly higher prevalence of dental caries and gingival bleeding among migrant children coming from Asian and African countries compared to the population of the host countries (European countries). The same study highlighted a general poor knowledge of oral health among migrants and a lack of monitoring by migrant parents of the oral health of their children. [PERSON] et al. [23] investigated the dental care utilization among Hispanic migrant farm-workers in South Florida, showing that most of them did not have a past year dental visit, and they presented poor oral conditions, having difficulties in accessing healthcare services. The article by [PERSON] et al. [24] assessed poor oral hygiene in half of the analysed migrant children coming from Somalia to Sweden, underlining that 78-82% of them had never visited a dental department in their native nation, and that, in most cases, parents did not assist their youngest children during tooth cleaning. There are different risk factors, which prevent migrant children from maintaining good oral health, and [PERSON] et al. [25] grouped them into 3 levels: (1) the child level, due to the different oral hygiene habits, (2) the family level, including economic barriers, parents' inadequate knowledge of oral health practices, and scant attention to the oral health condition of their children and (3) the community level, that is, lack of medical insurance. Considering these, interventions aimed at reducing disparities in access to dental treatment are necessary [26; 27; 28]. This observational study aimed to assess the periodontal health status and the prevalence of dental caries and lesions of the oral mucosa of a migrant cohort coming from middle- and low-income countries to Italy. Furthermore, we investigated the association between dental caries, periodontal status, and educational level of the included migrants.
## 2 Materials, Equipment and Context
### Haughton Mars Project on Devon Island  
The Haughton-Mars Project (HMP) is an interdisciplinary and international field research project making use of the Haughton crater impact structure and surrounding terrain as a terrestrial environmental analog for Mars and the moon. The Haughton Mars Project Research Station (HMP RS) is located in a remote polar desert on Devon Island, an uninhabited island in the territory of Nunavut in the Canadian High Arctic. The geologic features and biological attributes of Devon Island in general, and the HMP RS site in particular, offer a unique research and operations environment appropriate to the development of approaches, technologies, and field-based operational methodologies that might be critical to successful extraterrestrial human missions [24]. The HMP RS is managed and operated jointly by the Mars Institute and the SETI Institute with support from the Canadian Space Agency and NASA.
# Computational Aspects of \(L_{0}\) Linking in the Rasch Model
## 2 \(L_{0}\) Linking in the Rasch Model
### Statistical Properties of the Estimated Linking Parameter in \(L_{0}\) Linking  
We now investigate the statistical properties of the linking parameter estimate \(\hat{\mu}\) obtained from (10). The difference in estimated item difficulties required in the \(L_{0}\) linking approach can be written as  
\[\widehat{b}_{i2}-\widehat{b}_{i1}=-\mu+\kappa_{i}+u_{i} \tag{12}\]  
with fixed DIF effects \(\kappa_{i}\) and sampling errors \(u_{i}\), where \(\mathsf{E}(u_{i})=0\) and \(\mathsf{Var}(u_{i})=V_{i}\). Asymptotic unbiasedness follows from the general properties of ML estimation.  
The \(L_{0}\) linking approach is analyzed by starting from the estimating equation  
\[\frac{1}{I}\sum_{i=1}^{I}\rho_{\varepsilon}^{\prime}\big{(}\widehat{b}_{i2}- \widehat{b}_{i1}+\hat{\mu}\big{)}=\frac{1}{I}\sum_{i=1}^{I}\rho_{\varepsilon} ^{\prime}(\hat{\mu}-\mu+\kappa_{i}+u_{i})=0. \tag{13}\]  
Now, we apply a [PERSON] expansion with respect to \(\mu\), as in standard M-estimation theory ([48]; see also [25; 49]), and obtain  
\[0=\frac{1}{I}\sum_{i=1}^{I}\rho_{\varepsilon}^{\prime}(\hat{\mu}-\mu+\kappa_{ i}+u_{i})\simeq\frac{1}{I}\sum_{i=1}^{I}\rho_{\varepsilon}^{\prime}(\kappa_{i}+u_ {i})+\frac{1}{I}\sum_{i=1}^{I}\rho_{\varepsilon}^{\prime\prime}(\kappa_{i}+u _{i})(\hat{\mu}-\mu)=0. \tag{14}\]  
The bias and variance of \(\hat{\mu}\) can be derived from (14) and are given by  
\[\mathsf{Bias}(\hat{\mu})=-\frac{\sum_{i=1}^{I}\mathsf{E}\big{[}\rho_{ \varepsilon}^{\prime}(\kappa_{i}+u_{i})\big{]}}{\sum_{i=1}^{I} \mathsf{E}\big{[}\rho_{\varepsilon}^{\prime\prime}(\kappa_{i}+u_{i})\big{]}}\text { and } \tag{15}\]  
\[\mathsf{Var}(\hat{\mu})=\frac{\sum_{i=1}^{I}\mathsf{Var}\big{[}\rho_{ \varepsilon}^{\prime}(\kappa_{i}+u_{i})\big{]}}{\left\{\sum_{i=1}^{I}\mathsf{ E}\big{[}\rho_{\varepsilon}^{\prime\prime}(\kappa_{i}+u_{i})\big{]}\right\}^{2}}, \tag{16}\]  
where approximate independence of item parameters across items is assumed in (16). As a summary precision measure of the linking parameter estimate, the mean squared error (MSE) can be determined as  
\[\mathsf{MSE}(\hat{\mu})=\mathsf{E}(\hat{\mu}-\mu)^{2}=\mathsf{ Bias}(\hat{\mu})^{2}+\mathsf{Var}(\hat{\mu})=\frac{\sum_{i=1}^{I}\Big{\{} \big{(}\mathsf{E}\big{[}\rho_{\varepsilon}^{\prime}(\kappa_{i}+u_{i})\big{]} \big{)}^{2}+\mathsf{Var}\big{[}\rho_{\varepsilon}^{\prime}(\kappa_{i}+u_{i}) \big{]}\Big{\}}}{\left\{\sum_{i=1}^{I}\mathsf{E}\big{[}\rho_{\varepsilon}^{ \prime\prime}(\kappa_{i}+u_{i})\big{]}\right\}^{2}}. \tag{17}\]  
Although it might not be immediately evident from Equations (15)-(17), the bias, variance, and MSE depend on the choice of the tuning parameter \(\varepsilon\) in the \(L_{0}\) approximation \(\rho_{\varepsilon}\).  
In the next two sections, we compare the two differentiable approximations--the ratio and Gaussian loss functions--regarding their statistical properties in a numerical illustration and a simulation study. In particular, we focus on the choice of the tuning parameter \(\varepsilon\).
###### Abstract  
Concrete-encased steel composite columns are preferred for their exceptional ductility and strength, particularly in high-rise buildings. This research aims to enhance both the strength and ductility of these composite columns by increasing the height of the steel profile. Typically, hexagonal or circular openings, referred to as Castellated elements, are incorporated into the steel profile to achieve this height increase. This study employed a topology optimization method to identify the ideal opening shape for the steel profile in concrete-encased steel composite columns. The analysis revealed a sinusoidal-like opening shape, which was then refined for manufacturing. The optimal opening shape was used to increase the height of the existing steel profile, and nonlinear analyses were conducted to evaluate the effectiveness of this new optimized steel profile in concrete-encased steel composite columns. Two concrete-encased steel composite columns were designed: one with the optimal steel profile and the other with a standard steel I profile. ANSYS APDL 19.0 software was used to simulate an experiment based on an existing concrete-encased steel column to validate the nonlinear analysis. The verification analysis demonstrated a remarkable similarity between the experimental and numerical load-displacement graphs, indicating that the numerical analysis was reliable. In the analysis of the composite columns, both axial and lateral forces were applied in the nonlinear analyses. The axial force was applied at 15% of the column's capacity, while the lateral force was applied until the composite column reached a state of failure. The results of the nonlinear analyses allowed for a comparison of load-displacement curves and the performance of the composite columns. In comparison to the standard steel I profile, the steel profile with the optimal opening shape increased load-carrying capacity by approximately 19% and energy absorption capacity by approximately 24%.  
composite column; topology optimization; finite element analysis; nonlinear modeling +
Footnote †: journal: Applied Sciences
# Star Identification Algorithm Based on Dynamic Distance Ratio Matching
## 3 Star Identification Algorithm
### Initial Identification  
During the initial identification process, the star closest to the center of the FOV is selected as the reference star \(R\). As shown in the Figure 2, the reference star \(R\) serves as the center of the coordinate system, with other stars (neighboring stars) within the surrounding FOV designated as neighboring stars. Next, the planar distances \(\{r_{1},r_{2},\ldots,r_{N}\}\) between the reference star and its neighboring stars, as well as the angles \(\{\theta_{1},\theta_{2},\ldots,\theta_{N}\}\) between each pair of neighboring stars, are calculated sequentially. Based on the planar distances between the reference star and the neighboring stars, the corresponding distance ratios \(\{d_{r1},d_{r2},\ldots,d_{r3}\}\) are further obtained.  
Using the ratio of distances instead of absolute distances enhances the robustness against focal length variations. Changes in focal length do not affect the distance ratios between the stars.  
\[d_{ri}=\left\{\begin{array}{ll}\frac{r_{i+1}}{r_{i}}&\text{for}&i=1,2,\ldots,N-1\\ \\ \frac{r_{1}}{r_{N}}&\text{for}&i=N\end{array}\right. \tag{7}\]  
The distance ratios and angles corresponding to the reference star to be identified are used as initial identification features \(\{R_{k},\theta_{k}\}\), and a 2D lookup table is utilized to accelerate the search. It is quantified as \(\{i,j\}\) according to Equation (6), allowing for rapid identification of candidate navigation stars that meet the criteria through indexing. Simultaneously, to enhance the robustness of the identification algorithm against noise and prevent noise-induced stars from falling into other ranges, the effective candidate star \(star_{candidate}\) search area is expanded to a nine-grid pattern centered around the star \(\{i,j\}\) during the search process.  
\[\{star_{id},index_{k}\}_{cod}=\bigcup_{\begin{subarray}{c}i-1\leqslant m \leqslant i+1\\ j-1\leqslant n\leqslant j+1\end{subarray}}LUT(m,n) \tag{8}\]  
After the initial identification process, promising candidate stars that meet the matching requirements have been selected from the navigation star list. Additionally, the corresponding order of the current matching angles with respect to the candidate stars has been established, laying the foundation for the subsequent similarity calculation.
## 2 Materials and Methods
### Proposed Method
#### 2.3.5 Evaluation Parameters  
The evaluation was carried out using two different parameters. The first evaluation parameter was the Frechet Inception Distance or abbreviated as FID. This parameter is one of the most frequently used metrics in evaluating generative models. FID uses the InceptionV3 model to generate a multivariate embedding distribution of two images. If the two images are very similar, then the resulting FID will be very low and close to 0. Conversely, a high FID means that the two images have very different distributions. In this study, the expected generator was a generator that could create an image that was very similar to the ground truth. Therefore, the use of FID was very relevant for this purpose, and the expected value of FID was close to 0.  
The second parameter used was Structural Similarity Index Measurement (SSIM). SSIM works by comparing the structure of the image generated by the generator with ground truth. This study used SSIM because the resulting image was to be made as close as possible to the ground truth. Therefore, using SSIM, one can see how similar the resulting image is to the ground truth. The SSIM value was calculated and would produce a value from 0 to 1, where 1 meant that the two images compared were identical.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Scenario** & **Photo for Color Transfer** & **Gamma** \\ \hline Pale White & Not applied & 0.8 \\ \hline Fair & 0.85 \\ \hline Tan & 1.2 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Color Transfer Configuration. Photos by Generated Photos [17].  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline
**Scenario** & **a** & **b** & **c** & **d** & **e** & **f** & **g \({}^{*}\)** & **h** & **i** & **j** & **k** \\ \hline \(\lambda_{1}\) & 0.01 & 0.1 & 1 & 10 & 100 & 150 & 100 & 150 & & 100 & \\ \hline \(\lambda_{2}\) & & & 0.00001 & & & 0 & 0.0001 & 0.001 & 0.0001 & 0.00005 \\ \hline \hline \end{tabular}  
* This is the baseline method, i.e., cGAN (pix2 pix) by [PERSON] et al. [8].  
\end{table}
Table 2: Tuning-parameter scenarios.
## 2 Materials and Methods
### CT Acquisition Protocol  
Each patient with a histological diagnosis of gastric cancer underwent a pre- and post-p-ChT total body CT scan with contrast medium injection. CT scans were acquired on 128-slice CT (GE Revolution EVO Slice CT Scanner, GE Medical Systems, Milwaukee, WI, USA) in supine position, at end-inspiration and with cranio-caudal scanning. For the unenhanced imaging, arterial and delayed phase Z-axis coverage included the diaphragm apex to the iliac bone, and the portal-venous phase was scanned from the sovraclavear space to the public symphysis. For the purposes of this study, only the portal-venous phase was assessed.  
All patients underwent gastric lumen distention by drinking 4 glasses (125 mL) of water directly before the CT image acquisition. Contrast medium intravenous injection was tailored for each patient according to lean body weight (LBW): each patient received 0.7 gI/kg LBW [22; 23] and the result divided by contrast medium concentration (mgI/mL) obtaining the volume of administration. Iso-osmolar non-ionic contrast medium (iodixanol 320 mgI/mL, Visipaque 320; GE Healthcare, Cork, Ireland) was administered through an 18-20 gauge antecubital intravenous access, followed by 40 mL of saline solution with a contrast media injection system (Medrad(r) Centargo, Bayer) with a flow rate of 3 mL/s. Post-contrast CT scan timing was based on the bolus-tracking method (Smart Prep, GE, Milwaukee, WI, USA) by placing a 150 HU-threshold region of interest (ROI) within the lumen of the abdominal aorta at the celiac tripod level. The late arterial phase (18 s after threshold reached), portal-venous phase (70 s after the threshold reached), and delayed phase (180 s after threshold reached) were performed for each patient's unenhanced phase.  
CT technical parameters were: tube voltage 100 kV; tube current modulation was applied by using SMART mA (GE Healthcare, Milwaukee, WI, USA) ranging from 130 to 300 mAs; spiral pitch factor 0.98; section collimation 64 \(\times\) 0.625 mm; rotation time 0.6 s. All CT images were reconstructed with slice thickness of 1.25 mm with standard soft tissue reconstruction, by applying iterative reconstruction at 50% (ASiR-V, GE Healthcare, Milwaukee, WI, USA). No other iterative reconstruction was analyzed [24].
# Monitoring Safron Crops with UAVs
## 3 Field Study Design Methodology
### Site Selection  
The data used were RGB and multispectral images collected with the help of UAVs and processed by the method of photogrammetry to obtain the necessary information from the fields. The images came from 6 different crop fields of Kozani Saffron; the first two fields were in the harvest period in October 2021, while the remaining 4 were in the period between June and July 2021. These crops produce organic saffron. The selection of the specific fields was based on the fact that they are located far from each other and are a representative sample from all areas of the Prefecture of Kozani. In this way, it is possible to study different soil samples influenced by environmental elements, such as the presence of a lake, and by the climatic conditions of each region. Table 1 shows the coordinates for each field, the number of flights, and the number of photographs taken.  
Figure 2: Characteristics of the field: (**a**) saffron flower, (**b**) mouse trace, (**c**) weed, and (**d**) ground-truth data.
## 2 The Binary Exponent Backoff (BEB) Algorithm  
The 802.15.4 standard defines the specifications of the medium access control (MAC) protocol in low-rate wireless networks [5]. In this paper, we focus on the slotted CSMA-CA mechanism of the 802.15.4 MAC protocol. In this mechanism, a superframe structure is used to organize the communications over the wireless medium. The superframe is composed of a contention access period (CAP) and an optional contention-free period (CFP) (which offers collision-free guaranteed time slots (GTSs)). During the CAP, nodes utilize the slotted CSMA-CA mechanism to resolve medium access contention. The latter resolution is achieved through the binary exponent backoff (BEB) algorithm. BEB operates as follows. Before any transmission attempt, three parameters are initialized, namely, the number of backoff stages (NB = 0), the contention window (CW = 2), and the backoff exponent (BE = macMinBE) (macMinBE is an attribute defined in IEEE 802.15.4; its default value is 3). After that, the node backs off for a random duration selected from the range \(\left[0,2^{BE}-1\right]\). Once the backoff period expires, the node checks if the medium is clear during two consecutive clear channel assessments (CCA1 and CCA2). If the medium is clear in both CCAs, packet transmission will begin. If either of the CCAs reveals that the medium is busy, the value of BE will be increased by 1 (up to a maximum of macMaxBE (macMaxBE is an attribute defined in IEEE 802.15.4; its default value is 5)), CW will be reinitialized, and the node will backoff again; that is, NB is increased by 1 until it reaches a maximum of macMaxCSMABackoffs (macMaxCSMABackoffs is an attribute defined in IEEE 802.15.4; its default value is 4). If BE reaches its maximum, it cannot change unless a successful/failed packet transmission occurs or packet retransmission commences. In that case, BE is reinitialized. The packet is dismissed if the macMaxCSMABackoffs threshold is crossed, and the BEB process will start over. Once a packet is transmitted successfully, an ACK packet is expected. If the ACK packet is not received, the node attempts (to a maximum of macMaxFrameRetries attempts) to retransmit the packet using the BEB algorithm. If macMaxFrameRetries (macMaxFrameRetries is an attribute defined in IEEE 802.15.4; its default value is 3) is crossed, the packet is dismissed. The time unit used by CSMA-CA is the aUnitBackoffPeriod (aUnitBackoffPeriod is a constant defined in IEEE 802.15.4 and is equal to 320 microseconds). In Figure 1, we show the flow diagram of the slotted CSMA-CA mechanism.  
The 802.15.4 MAC is distinguished by the backoff mechanism and the use of two CCAs. These characteristics lead to a reduced likelihood of collisions and an improved conservation of power, and these are useful characteristics in WSN-based applications like WBANs.
## 1 Introduction
As demonstrated in some previous studies [36; 44; 45; 46], the presence of MIS, PES, and other potential confounding factors, such as ultraviolet light and high temperature, contributes to the difficulty in the analyses of fingernail ESR signals. Not surprisingly, numerous attempts have been carried out to circumvent BKG, while RIS remains intact. For example, it was found that water treatment worked effectively in erasing the BKG (e.g., [25; 29; 30; 35; 38; 47; 48; 49]). The application of an antioxidant to freshly cut fingernails was also investigated; it was reported that the 0.1 M dithiothreitol (DTT) reducing reagent with 20 min treatment time reduced both MIS and BKG [26]. It is still unclear, however, if the DTT treatment would be effective for fingernail samples that were stored for a long time. Long storage in an ambient, humid conditions could cause unstable RIS, as well as changing BKG [29; 43]. The difficulty in keeping the stability of RIS and BKG has led some authors to investigate other variants, such as storage in the freezer at low (sub-zero) temperature [31], in a vacuum desiccator with 0% air humidity [35; 36; 38; 49], or in olive oil with low water content [39]. Since research on such storage mediums started very recently, further comparative studies are desirable to clarify the efficacy for improving the signal stability of RIS and BKG in conjunction with possible chemical treatments. The present study pursues this direction and tries to contribute to the development of a practical protocol of fingernail ESR dosimetry for routine monitoring of the extremity exposures of medical workers handling radiation sources. Here we investigate the effects of chemical treatment using DTT on the changes of the ESR spectra in irradiated and unirradiated fingernails after a long storage time. The efficacy of DTT in the reduction in intra-individual variations of BKG is also examined through comparative experiments using chemically and non-chemically treated samples stored in two different mediums (i.e., vacuum chamber and freezer).
## 4 Case Study
### Result and Analysis
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Method**} & **Average Voltage** & \multirow{2}{*}{**Action Power Loss**} & **Voltage Overrun** \\  & **Deviation** & & **Rate** \\ \hline Without RPO & 0.0386 & 24.833 MW & 36.55\% \\ DDPG [31] & 0.0131 & 17.202 MW & 4.92\% \\ TD3 [27] & 0.0250 & 16.817 MW & 5.14\% \\
**Proposed** & **0.0073** & **11.544 MW** & **0.00\%** \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Hyperparameter values. Figure 8: The learning process of agents under three DRL approaches of Proposed, DDPG, and TD3. To determine the optimal GCN depth, we conducted an ablation experiment comparing models with 1-, 2-, 3-, and 4-layer GCN configurations. Under identical training conditions, each model was trained multiple times. The results are presented in Figures 12 and 13. Figure 13 shows the reward curves during training for each GCN variant, while Figure 12 summarizes the peak reward, training time, and number of episodes to convergence. As illustrated, the 2-layer GCN model achieved the highest reward and the fastest learning,  
Figure 11: The active power loss of 96 time intervals over the next day using the different methods. Figure 10: Comparison of test results: TD3 and Proposed. Figure 9: Comparison of test results: Without RPO and DDPG. converging in approximately 45 min and 100 episodes. Although the deeper GCNs could extract more detailed features, they offered negligible gains in reward at the cost of substantially longer training times. Conversely, the single-layer GCN model exhibited significantly lower performance; despite facilitating basic feature extraction, its limited topological awareness slowed convergence and led to reduced final rewards. These findings highlight the effectiveness of integrating GCN-based feature extraction with a cooperative reward mechanism to enhance the agent's representational capacity. The ablation study confirmed that each component independently contributed to performance gains, and their combined implementation produced the most robust and efficient recovery strategy. Figure 12: Reward curves for training rounds under different GCN layers. Figure 13: Comparison curves of rewards, training time, and convergence rounds under different GCN layers.