esa-sceva/satcom-chunk-collection huggingface dataset:
# Design and Application of a New Visible Light-Curing 3D Printing Platform
## 3 Results
### Formulation and Morphology Analysis of Energetic Power Column  
To produce the energetic paste, solid filler was combined with the visible light-curing paste. This mixture was stirred for 1 h at a rotation speed of 500 r/min, followed by 5 min of ultrasonic dispersion. Various visible light-curing pastes, with solid contents of 85%, 80%, 75%, and 70%, were prepared and assessed. Table 5 showcases the results to determine the formation consistency of these pastes.  
Table 5 reveals that with a solid content of 85%, the energetic slurry remains unformed. However, at 80% solid content, it takes shape. Therefore, 80% was chosen as the ideal solid content for the energetic slurry. Using the standard deviation as the assessment metric [16; 17], the preparation of the energetic drug column considered four primary factors: the quality of RDX (A), AP (B), Al (C), and the visible light-solidified slurry (D). An analysis based on orthogonal experiments helped determine the best formula [18; 19]. Details of these orthogonal factor levels are presented in Table 6, while Table 7 displays the standard deviations derived from the orthogonal test results.  
It can be seen from Table 7, with the standard deviation as the evaluation index, the order of influence of the four factors is visible light-solidified slurry > RDX > AP > Al, and the R value of visible light-solidified slurry is 0.11, which has the greatest influence; that is, A\({}_{3}\)B\({}_{1}\)C\({}_{1}\)D\({}_{2}\) is the optimal energetic slurry formula. A 20% solid content was established for the energetic slurry. The composition of this slurry includes 9.57% RDX, 57.45% AP, 11.7% Al, and 21.28% of the visible light-curing slurry. The slurry cures in 30.5 s, with a curing depth of 2.3 mm and a hardness rated at HB. Figure 10 provides a visual of the energetic paste. The casing measures \(\Phi\)6 mm \(\times\) 6 mm with a wall thickness of 0.5 mm. Post-charging, the energetic power within the shell retains the size of \(\Phi\)6 mm \(\times\) 6 mm. This charged energetic column within the shell is depicted in Figure 11.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **A (g)** & **B (g)** & **C (g)** & **D (g)** \\ \hline Level 1* & 5 & 54 & 11 & 18 \\ Level 2* & 7 & 57 & 13 & 20 \\ Level 3* & 9 & 60 & 15 & 22 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Level table of factors for orthogonal test of energetic slurry.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Name** & **Value** \\ \hline Curing tim (s) & 7.5 \\ Viscosity (mPa-s) & 226 \\ Curing hardness (H) & 2 \\ Gel ratio (\%) & 96.67 \\ Percentage of contraction (\%) & 98.35 \\ Curing depth (mm) & 9.41 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Visible light-curing paste formula performance parameters.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Solid Content** & **Modling Degree** \\ \hline
70\% & Successful \\
75\% & Successful \\
80\% & Successful \\
85\% & Failed \\ \hline \hline \end{tabular}
\end{table}
Table 5: Curing degree of energetic slurry with different solid content.
## 2 System Model
### User Association  
Each UE is assumed to be associated with only one SBS, but each SBS can serve multiple UEs using OFDMA. The UEs served by the same SBS constitute a _cluster_. Let \(\mathcal{F}_{k}\) denote the set of subchannels allocated to the \(k\)th UE and \(c_{j,k}\in\{0,1\}\) represent the status of user association, i.e., \(c_{j,k}=1\) if the \(k\)th UE is associated with the \(j\)th SBS and \(c_{j,k}=0\) otherwise. Then, the set of UEs in the cluster \(j\) is given by \(\mathcal{C}_{j}=\{k:c_{j,k}=1,k\in\mathcal{K}\}\), with \(|\mathcal{C}_{j}|\) being the number of UEs in \(\mathcal{C}_{j}\). The SBS serving the \(k\)th UE can be represented by \(\mathcal{S}_{k}=\{j:c_{j,k}=1,j\in\mathcal{J}\}\). Note that, since each user is assumed to be associated with only one SBS in this paper, \(|\mathcal{S}_{k}|\) equals one. The set of active SBSs is \(\mathcal{J}^{active}=\{j\mid|\mathcal{C}_{j}|>0\}\).  
The spectral efficiency of the \(k\)th UE is given by  
\[\rho_{k}=\sum_{f\in\mathcal{F}_{k}}\log_{2}\left(1+SINR_{k,f}\right), \tag{1}\]  
where the signal-to-noise-plus-noise ratio (SINR) is  
\[SINR_{k,f}=\frac{\sum_{j=1}^{J}c_{j,k}g_{j,k,f}P_{j,k,f}}{\sigma^{2}+l_{k,f}}\]  
Figure 1: An illustration of the considered wireless network. Each cluster consists of an SBS and its serving UEs.  
with \(g_{j,k,f}\) being the channel gain between the \(j\)th SBS and the \(k\)th UE in subchannel \(f\), \(I_{k,f}\) the interference observed by the \(k\)th UE, and \(\sigma^{2}\) the noise power. Specifically, the channel gain is defined as \(g_{j,k,f}=|h_{j,k,f}|^{2}\), where \(h_{j,k,f}\) is the corresponding channel coefficient. The transmit power \(P_{j,k,f}\) from SBS \(j\) to UE \(k\) in subchannel \(f\) needs to satisfy the power constraint \(0\leq\sum_{k\in C_{j}}\sum_{f\in F_{k}}P_{j,k,f}\leq P_{SBS_{j},max}\), where \(P_{SBS_{j},max}\) is the maximum transmit power of the \(j\)th SBS. The user sum-rate for the \(j\)th cluster is given by  
\[\rho_{j}^{SBS}=\sum_{k\in C_{j}}\rho_{k}=\sum_{k=1}^{K}c_{j,k}\rho_{k}. \tag{2}\]  
We consider the scenario that each SBS allocates orthogonal subchannels to different UEs within its serving coverage, so there is no intra-cluster interference in each cluster. Each UE can acquire at least one subchannel for data transmission if the cluster size (i.e., the number of served UEs) is not larger than the number of subchannels. Without intra-cluster interference, the interference term \(l_{k}\) is composed only by the _inter-cluster_ interference and can be expressed by  
\[l_{k,f}=\sum_{u\
otin C_{\text{S}_{k}}}\sum_{f\in F_{k}(\uparrow),F_{u}}g_{j,k,f}P_{j,u,f}. \tag{3}\]  
Detailed notation descriptions are summarized in Table 2.
## 2 Materials and Methods  
### Patients and Data Collection  
We conducted a retrospective cohort study including all patients fulfilling the following criteria:  
* Inpatient stay in our tertiary referral centre (Immanuel Krankenhaus Berlin Buch) between 1 January 2006 and 31 December 2015
* Diagnosis of a SARD: SLE, primary or secondary Sjogren's syndrome (SS), systemic sclerosis (SSc; diffuse, limited, overlap with myositis), polymysitis (PM), dermatatomyositis (DM), anti-synthetas syndrome, undifferentiated connective tissue disease (UCTD) or mixed connective tissue disease (MCTD).
* Abdominal ultrasound examination  
From their medical records, we collected the following data:  
* Patient characteristics including sex, age, disease duration, former organ manifestations and SARD entity.
* Some patients had a diagnosis of rheumatoid arthritis (RA), secondary SS or an antiphospholipid syndrome (APLS) in addition to their main SARD diagnosis. Their percentage is given in Table 1, labelled as \"additional [diagnosis of]\".
* Results from abdominal ultrasound examination
* The treating clinician's interpretation of the ultrasound examination results and their relevance to the therapy strategy as described in the hospital discharge letter
* Immunosuppressive therapy at the time of admission and discharge from the clinic that involved glucocorticoid dose (calculated as prednisolone equivalent), type and dose of disease-modifying anti-rheumatic drugs (DMARDs).  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline \multicolumn{2}{c}{**All**} & \multicolumn{2}{c}{**Patients**} & \multicolumn{2}{c}{**SLE**} & \multicolumn{2}{c}{**SS**} & \multicolumn{2}{c}{**SSc**} & \multicolumn{2}{c}{**MSositis**} & \multicolumn{2}{c}{**UCTD**} & \multicolumn{2}{c}{**MCTD**} \\ \hline  & & & & **Prim.** & **Sec.** & **Diffuse** & **Lim.** & \begin{tabular}{c} **with** \\ **Myos.** \\ \end{tabular} & **PM** & \begin{tabular}{c} **DM** \\ **DM** \\ \end{tabular} & \begin{tabular}{c} **Anti-** \\ **Synthetase** \\ \end{tabular} & & \\ \hline \
\begin{tabular}{c} Number of \\ patients \\ \end{tabular} & 1092 & 338 & 335 & 250 & 150 & 197 & 34 & 40 & 43 & 37 & 122 & 149 \\ \hline
\begin{tabular}{c} Percentage of \\ cohort (\%) \\ \end{tabular} & 100 & 19.9 & 19.8 & 14.8 & 8.9 & 11.6 & 2.0 & 2.4 & 2.5 & 2.2 & 7.2 & 8.8 \\ \hline \hline add. RA (\%) & 15.0 & 5.2 & 2.8 & 82.6 & - & 4.0 & 5.9 & - & - & - & 2.0 & 1.2 \\ \hline add. AFLS (\%) & 5.6 & 23.2 & 0.8 & 0.6 & 1.3 & 0.8 & - & - & - & 6.3 & 4.0 & 6.9 \\ \hline add. Sjogren (\%) & 11.8 & 33.0 & - & - & 15.4 & 18.4 & 17.7 & 4.2 & - & 31.3 & 5.1 & 18.4 \\ \hline New diagnosis (\%) & 33.2 & 17.5 & 40.9 & 20.4 & 30.8 & 40.0 & 53.9 & 41.7 & 42.4 & 31.3 & 51.5 & 33.3 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Patient characteristics.  
Data were collected in accordance to the Declaration of Helsinki. The ethics board of the Berlin Board of Physicians (Berliner Arztekammer) approved the study (Eth-23-16).
## 1 Introduction
To address these questions, we conduct extensive experiments on the DFDC [12] and FaceForensics++ [11] datasets. The DFDC dataset, developed with contributions from leading technology companies, includes a diverse collection of manipulated and real videos, offering a benchmark for assessing detection algorithms. FaceForensics++, on the other hand, provides a controlled environment with various manipulation methods and compression levels, enabling a thorough evaluation of model generalization across different deepfake generation techniques. Moreover, we consider the vulnerability of these networks to adversarial examples, emphasizing the practical importance of adversarial robustness in safeguarding content authenticity. The key contributions of this study are as follows:  
* Comprehensive Model Comparison: We provide a detailed performance analysis of XCeption, ResNet, and VGG16 across multiple datasets, using metrics such as accuracy, precision, recall, F1-score, AUC-ROC, and Matthews Correlation Coefficient(MCC). This evaluation offers a nuanced understanding of each model's strengths and limitations. * Cross-Dataset Generalization: By testing the models on DFDC and FaceForensics++, we assess their ability to generalize to new data distributions, which is a critical requirement for real-world deployment. * Adversarial Robustness Evaluation: We evaluate the resilience of these models against adversarial perturbations generated using the Fast Gradient Sign Method (FGSM), quantifying the impact of such attacks on detection performance. * Trade-Off Analysis: We explore the trade-offs between detection accuracy and processing speed, providing practical insights into the suitability of each model for real-time detection and forensic applications. The findings of this study have significant implications for the development and deployment of deepfake detection systems. By systematically comparing leading architectures under diverse conditions, we identify practical strategies for mitigating their limitations and enhancing their utility in real-world scenarios. Additionally, we underscore the need for novel strategies--such as ensemble techniques, domain adaptation, and defenses tailored to diffusion-based forgeries--to keep pace with the increasing sophistication of generative models.
_Appl. Sci._**2012**, \(2\), 549-557; doi:10.3390/app2020549  
_Applied sciences_  
**ISSN 2076-3417**  
www.mdpi.com/journal/applsci  
**Effects of Nonlinear Chirp on the Self-Phase Modulation of Ultrashort Optical Pulses**  
**[PERSON] * and [PERSON]**  
Department of Physics, Faculty of Science and Technology, Tokyo University of Science,  
2641 Yamazaki, Noda, Chiba 278-8510, Japan  
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS]; Tel.: +81-4-7122-1447; Fax: +81-4-7122-1495.  
_Received: 30 April 2012; in revised form: 28 May 2012 / Accepted: 29 May 2012 / Published: 4 June 2012_  
**Abstract:** In this article, we analytically investigate the spectral broadening by self-phase modulation of strongly chirped optical pulses. The dispersion due to the nonlinear optical process is expressed as functions of a linear and a nonlinear initial chirp. As a result, it is found that the third-order dispersion strongly depends on the initial linear chirp and the nonlinearity for self-phase modulation.  
**Keywords:** ultrashort optical pulse; self-phase modulation; nonlinear chirp
## 2 Related Work
Some implementations of RL in education focus on content adaptation, where the system selects the most appropriate learning materials based on past user performance, while others emphasize pedagogical decision-making, adjusting instructional strategies based on learning efficiency. However, one of the challenges of RL in education is defining appropriate reward functions and ensuring that the system's adaptations are pedagogically meaningful [37]. Many RL-based systems optimize broad learning outcomes but do not fine-tune individual user interface elements, such as hints, guidance, and difficulty adjustments, which are critical for personalized learning. While both fuzzy logic and reinforcement learning have been explored separately in educational contexts, relatively few studies have focused on dynamic fuzzy weight adjustment using RL [38]. Fuzzy weight adjustment in education aims to refine how adaptive systems make decisions by continuously updating fuzzy membership functions based on real-time student behavior [39]. In conventional fuzzy logic applications, membership functions and weight parameters are often manually set by experts, requiring prior assumptions about student behavior that may not always be accurate. Dynamic fuzzy weight adjustment, however, allows these parameters to evolve based on actual user interactions, making the adaptation process more data-driven. Some research has explored hybrid fuzzy-RL approaches, where RL optimizes the pedagogical strategy, but fewer works have directly integrated RL for the fine-grained adjustment of fuzzy weights in adaptive UI elements [40, 41, 42, 43, 44, 45]. This integration is crucial because the thresholds that determine when a UI adaptation occurs (such as when hints appear or difficulty increases) need continuous refinement to remain effective for different learners. Compared with previous research, this work differs by introducing a reinforcement learning-driven approach for dynamically adjusting fuzzy weights in adaptive user interfaces. Instead of using static membership functions and manually assigned fuzzy weights, the proposed system learns over time by optimizing when UI modifications should take place. Unlike prior fuzzy-based educational systems, where intervention strategies are predefined and remain fixed, this work ensures that thresholds for UI adaptation change based on actual learning behavior. Additionally, while reinforcement learning has been applied to educational content sequencing and recommendation, the use of RL specifically to directly optimize each type of UI-specific adaptation, such as hint display, step-by-step guidance, and difficulty adaptation, has not been attempted before. The integration of fuzzy logic and reinforcement learning in this way enables a more flexible, personalized, and continuously improving learning experience, making adaptive user interfaces more effective and responsive in educational software.
# Design of Inverted Nano-Cone Arrayed SERS Substrate for Rapid Detection of Pathogens
## 3 Results
### Nanocone Design and Material Selection for High Field Enhancement
Note that the top of the cone is free of coating to have concentrated photons will not be reflected and reach nanorods. As shown in Figure 1c, the electric field is highly confined around the nanorod at the top of the cone, forming plasmonic hot spots. Inside the cone, the electric vectors exhibit multi-reflection resonance patterns which show the cavity resonance mode. In Figure 3b, the electric field is also highly confined around the nanorod, but the peak intensities are much lower than the case in Figure 3a, indicating that Si film coated cavity design has 5 times higher electric field enhancement than the Au-coated cavity design has. Figure 3c shows the field enhancement performance comparison as a function of exciting light's wavelength at the top observing plane for Si-coated cone, Au-coated cone and flat surface cases. For this calculation, we segmented the domain into rectangular meshes to use FTDT method reported previously [27; 28; 29; 30]. Each mesh has its own optical property can represent the structure and the surrounding environment. A plane of mesh is chosen as the source plane where the plane wave light is generated numerically. In this plane, we set the magnitude of input electrical vector as \(E_{0}\). In the area close to the Au nanorod, we have chosen a group of meshes to record the optical vector data. From these meshes we can obtain the averaged electrical field intensity \(E\) as a function of wavelength and mesh number. We then select the maximum intensity among them and the mesh with the maximum electrical intensity at a given wavelength. We then plot this maximum intensity at different wavelengths as shown  
in Figure 3c. The simulation result shows that the Raman enhancement at 758 nm can reach up to 10\({}^{8}\) for the Si-coated case, 10\({}^{6}\) for the flat surface case and 10\({}^{3}\) for the Au-coated case. With the help of low-loss photon-concentrating Si-coating, Raman sensitivity of the nanorods on nanocone can be improved over the nanorods on flat surface by 10\({}^{2}\). The loss at the Au film greatly reduces the electric field of the incident light, resulting field enhancement much lower than that of the nanorods with the flat surface.
# An Object Detection Method Based on Feature Uncertainty Domain Adaptation for Autonomous Driving
## 5 Ablation Study  
The proposed FUDA leverages feature uncertainty to represent the degree of uncertainty of the feature extraction network on the current data feature distribution. This approach guides the domain adaptation process at the feature level by mapping the uncertainty maps to the instance-level alignment, enabling the model to assign different domain adaptation weights for each ROI during training. To analyze the effectiveness of the proposed FUA and IUA, Table 4 shows the performance improvements in different modules on detection. Without the addition of FUA and IUA modules, the proposed model achieves the same results as DA, which is used as a baseline for comparison. The results show that the addition of the FUA module leads to a 25.9% mAP improvement. Furthermore, with the simultaneous addition of FUA and IUA modules, the mAP improves by 44.8%. The FUA module contributes significantly to domain adaptation, while the IUA module further amplifies the performance improvement.  
The visual results of Cityscapes to Cityscapes foggy are presented in Figure 9. It can be observed from Figure 9 that the model with the FUA module can detect vehicles and pedestrians that are affected by fog. Furthermore, the application of both the FUA and IUA modules enables the detection of distant targets. In contrast, the FRCNN model without domain adaptation can only detect large objects in close proximity. This demonstrates the effectiveness of the proposed FUA and IUA modules in providing domain adaptation.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Methods** & **Person** & **Rider** & **Car** & **Truck** & **Bus** & **Train** & **Motor** & **Bicycle** & **mAP** \\ \hline DA & 25.0 & 31.0 & 40.5 & 22.1 & 35.3 & 20.2 & 27.1 & 20.0 & 27.7 \\ FUA & 28.3 & 39.4 & 51.6 & **28.8** & 39.2 & **32.4** & **31.5** & 28.2 & 34.9 \\ \hline FUA + IUA & **43.9** & **49.1** & **53.9** & 28.4 & **41.8** & 31.6 & 28.2 & **45.0** & **40.2** \\ \hline \hline \end{tabular}
\end{table}
Table 4: Effects of FUA and IUA on model detection performance in Cityscapes to Cityscapes foggy. Bold indicates the highest result.  
Figure 8: The visual results of Cityscapes to real data.
# Parcel Classification and Positioning of Intelligent Parcel Storage System Based on YOLOv5
###### Abstract  
Parcel storage provides last-mile delivery services as part of the logistics process. In order to build an intelligent system for parcel storage, we conducted a study on parcel box recognition using AI's deep learning technology. Box detection and location estimation studies were conducted using the YOLOv5 model for parcel recognition, and this model is presently being applied to many studies because it has excellent object recognition and is faster than previous models. The YOLOv5 model is classified into small, medium, large, and charge according to the size and performance of the model. In this study, these four models were compared and analyzed to perform an experiment showing the optimal parcel box recognition performance. As a result of the experiment, it was determined that the precision, recall, and F1 of the YOLOv5 large model were 0.966, 0.899, and 0.932, respectively, showing a stronger performance than the other models. Additionally, the size of the YOLOv5 large is half that of YOLOv5 large, and the YOLOv5 large showed the optimal performance in object recognition of the parcel box. Therefore, it seems that the basis for building an intelligent parcel storage system, which shows optimal efficiency in real time using the YOLOv5 large model, can be laid through the parcel object recognition experiment conducted in this study.  
parcel storage system; e-commerce logistics; object detection; YOLOv5; box detection Article
# The SSP-Tree: A Method for Distributed Processing of Range Monitoring Queries in Road Networks
## 3 The Proposed Method
### The Segment-Based Space Partitioning Tree (SSP-Tree)
#### 3.2.1 Description
This is accomplished by the following lemma. Lemma 4: _Given a node \(N\) of the SSP-tree, a query \(q=(q.p,q.d)\), an object \(o\) moving only inside \(N\), and one of the \(q\)'s relevant query segments \(qs[p_{a},p_{b}]\) that fully intersects \(N\), suppose that \(o\) enters or leaves \(qs[p_{a},p_{b}]\). In such a case, \(o\) cannot affect the current result of \(q\)._  
Proof: From Definition 8, we know that both endpoints \(p_{a}\) and \(p_{b}\) of \(qs[p_{a},p_{b}]\) are outside \(N\). In addition, from Section 2.1, we know that \(\forall p\in qs[p_{a},p_{b}]\), \(dist_{N}(q.p,p)\leq q.d\). If \(o\) enters  
Figure 6: An example of the SSP-tree. \(qs[p_{a},p_{b}]\), it comes from one of the query segments that is connected with \(qs[p_{a},p_{b}]\) at the point \(p_{c}\), where \(p_{c}\in qs[p_{a},p_{b}]^{\circ}\) and \(p_{c}\) is inside \(N\), because \(o\) is moving only inside \(N\). Let this query segment be \(qs[p_{c},p_{d}]\). Then, \(qs[p_{c},p_{d}]\) must also be the relevant query segment of \(q\), i.e., \(qs[p_{c},p_{d}]\in QR(q)\), because \(dist_{N}(q.p,p_{c})<q.d\). Similarly, if \(o\) leaves \(qs[p_{a},p_{b}]\), it enters one of the query segments that must also be the relevant query segment of \(q\). Therefore, \(o\) cannot affect the current result of \(q\). For example, assuming the capability \(o_{1}.Cap\) of the moving object \(o_{1}\) in Figure 6 is 3, \(o_{1}\) is assigned the tree node \(N_{211}\) as its vicinity region together with only the query segment \(qs[p_{4},p_{5}]\). When \(o_{1}\) moves as shown in the figure, it sends only the RequestVR message to the server for receiving a new vicinity region. Then, before assigning \(o_{1}\) a new vicinity region (together with new query segments), the server should additionally check whether \(o_{1}\) leaves the original query segment \(qs[v_{5},v_{6}]\) by accessing \(N_{211}.FL\) because, if so, \(o_{1}\) may affect the current result of the query \(q_{1}\). In this example, \(o_{1}\) does not leave \(qs[v_{5},v_{6}]\). Please note that if the query segment \(qs[p_{4},p_{6}]\) is generated and assigned to \(o_{1}\), \(o_{1}\) should send two notification messages to the server, one that it has leaved \(N_{211}\) and the other that it has leaved \(qs[p_{4},p_{6}]\).