esa-sceva/satcom-chunk-collection huggingface dataset:
# Assessment of Skin Deep Layer Biochemical Profile Using Spatially Offset Raman Spectroscopy
###### Abstract  
Skin cancer is currently the most common type of cancer with millions of cases diagnosed worldwide yearly. The current gold standard for clinical diagnosis of skin cancer is an invasive and relatively time-consuming procedure, consisting of visual examination followed by biopsy collection and histopathological analysis. Raman spectroscopy has been shown to efficiently aid the non-invasive diagnosis of skin cancer when probing the surface of the skin. In this study, we employ a recent development of Raman spectroscopy (Spatially Offset Raman Spectroscopy, SORS) which is able to look deeper in tissue and create a deep layer biochemical profile of the skin in areas where cancer lesions subtly evolve. After optimizing the measurement parameters on skin tissue phantoms, we then adopted SORS on human skin tissue from different anatomical areas to investigate the contribution of the different skin layers to the recorded Raman signal. Our results show that using a diffuse beam with zero offset to probe a sampling volume where the lesion is typically included (surface to epidermis-dermis junction), provides the optimum signal-to-noise ratio (SNR) and may be employed in future skin cancer screening applications.  
Keywords:Raman spectroscopy; skin cancer; tissue phantoms; Spatially Offset Raman Spectroscopy (SORS) +
Footnote †: journal: Medical Physics
# Singulation of Objects in Cluttered Environment Using Dynamic Estimation of Physical Properties
## 5 Scattering Algorithm and Experimentation
Identification and visual segmentation of objects in a cluttered environment were important research issues. However, thanks to recent developments in deep learning techniques, the performance of semantic segmentation and instance segmentation algorithms [27; 28; 29] has improved significantly. Moreover, some of them provide open source code [30] to help other researchers to train their own datasets. Therefore, with the techniques mentioned above, it is straightforward to identify and segment objects in an image for robotic grasp. However, since this paper concentrated solely on the mechanic issue, we are currently assuming that locations of all parts are already known. Considering the recent development of segmentation, we believe that our methodology can be easily extended to scenarios that require object identification and segmentation processes. The scattering algorithm is explained in Figure 11. The initial positions of all objects are obtained from the real world and are fed to the virtual world. Then, the scattering experiment is performed in the virtual world until the desired distances among objects are achieved as defined in Equations (35) and (36). DAFUL [31] software, made of Virtual Motion Co., was employed in the virtual world simulations. Here, a feedback routine is employed to minimize the error between the desired distance and current distance by updating the colliding velocity of the robot manipulator. Once the error is within the allowed range, the objects are singulated. Finally, that same desired velocity will be given as the input to the robot manipulator and real-time scattering is performed. During experimentation, the minimum distance among objects is set as \(d_{\text{min}}\) = 90 mm. The average maximum and minimum maximum distance is set as \(S_{\text{min}}\) = 150 mm, \(S_{\text{max}}\) = 900 mm. Once all physical parameters are estimated, based on the ratio of masses and coefficient of restitutions, the change in velocity is calculated by using  
Figure 10: Impulse geometry: (**a**) normalized impulse ellipsoid for the Indy 7 robotics arm; (**b**) impulse ellipsoid for the four configurations; (**c**) maximum and minimum impulse direction. Figure 14: Scattering experiments for singulation of three objects by considering general shapes: (**a**) before collision; (**b**) after collision. Manipulator velocity: 0.4 m/s. Figure 13: Scattering experiments with three objects by considering circular shapes only: (**a**) before collision; (**b**) after collision. Manipulator velocity: 0.5 m/s. Figure 15: Scattering experiments for singulation of four objects by considering circular shapes only: (**a**) before collision; (**b**) after collision. Manipulator velocity: 0.6 m/s.
# Transparent CoAP Services to IoT Endpoints through ICN Operator Networks +
## 4 Design and Implementation
### ICN Operations  
A request from a CoAP client is translated to an appropriate ICN name which corresponds to the FQDN of the CoAP server, while the response to that request is a publication to the appropriate ICN name corresponded to the URL of the request. This enables a sNAP to subscribe to the FQDN of any attached CoAP server, while a sNAP can publish the CoAP response to the appropriate URL. Let us describe a simple example of a CoAP client request and CoAP server response. The sNAP starts subscribing to the hashed Host-URI of the CoAP server. Then, cNAP receives a CoAP request for the URL \"aueb.example.gr\"  
Figure 3: Namespaces for in the POINT architecture.  
which is published to the Rendezvous (RV). The RV looks up for matching the request with the server's subscription and requests the TM to create a forwarding path for the request as well as the reverse path. Eventually the TM sends the forwarding path (FID\({}_{req}\)) and reverse path (FID\({}_{res}\)) to the cNAP. The cNAP transfers the CoAP message to the sNAP using (FID\({}_{req}\)). The sNAP decapsulates the CoAP message and sends it to the CoAP server. The CoAP server receives the CoAP message and forwards the CoAP response to the sNAP. The sNAP forwards it to the cNAP using (FID\({}_{res}\)). Finally, the cNAP forwards the response to the appropriate client.
## 5 Simulation Results and Analysis  
In this section, the performance analysis of the algorithm is discussed and the results of some numerical examples are presented to evaluate the performance of the proposed waveform design method. A ULA consisting of \(M=32\) antenna elements having half-wavelength enter-element spacing has been considered at the transmitter and receiver sides. The radar target is located at \(\theta_{R}=0^{\circ}\) and the communication user at \(\theta_{C}=45^{\circ}\). The desired radar waveform is based on linear frequency modulation (LFM). Similarly, the desired communication waveform uses the QPSK modulation scheme. We have considered \(N_{s}=1\) symbols and \(N_{b}=2\) bits per symbol. Thus, each waveform carries 2 bits of information per pulse repetition interval (PRI).  
Different experiments are performed to evaluate the performance in different scenarios. Because both radar and communication receivers expect some desired waveforms, coherent detection can be used to match the received signal waveform with the desired waveform. Monte Carlo simulations are conducted to evaluate the performance of communication for different values of the signal-to-noise ratio (SNR).  
The proposed method is compared with the far-field radiated emission design (FFRED) [56], the iterative optimization technique (using directly normalized waveforms) [26], and the theoretical values. In the FFRED method, \(0\%,10\%\), and \(40\%\) of the total power is allocated to the orthogonal complement waveform, of which the FFRED-40% has the best performance. The authors of [26] proposed several waveform design methods. One method designed non-constant modulus waveforms and had a closed-form solution to the waveform design problem. They also proposed an iterative method for constant modulus waveforms. However, being computationally complex, they used the results of the first method using non-constant modulus waveforms and used iterative optimization for further refining those waveforms. They defined this method as 'directly normalized' in their simulations.
# Extended Depth-of-Field of a Miniature Optical Endoscope Using Wavefront Coding
## 3 Design and Simulation
### Initial Optical Endoscope
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameter** & **Data** & **Parameter** & **Data** \\ \hline Number of surfaces & 32 & Diameter of entrance pupil (mm) & 0.350 \\ \hline Number of apertures & 8 & Position of entrance pupil (mm) & 3.181 \\ \hline Effective focus distance (mm) & 3.212 & Diameter of exit pupil (mm) & 0.899 \\ \hline Back focus distance (mm) & 1.474 & Position of exit pupil (mm) & 13.727 \\ \hline F number of image space & 9.178 & Maximum of radial field of view (\({}^{\circ}\)) & 31.113 \\ \hline F number of work & 15.435 & Paraxial imaging height (mm) & 3.194 \\ \hline Diameter of aperture stop (mm) & 0.836 & Paraxial magnification & \(-\)1.664 \\ \hline Main wavelength (\(\mu\)m) & 0.586 & Angle magnification & \(-\)0.389 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Detailed information of the optical endoscope. Figure 1: Designed model of optical endoscope. The length and diameter of optical endoscope are 90 mm and 3 mm, respectively. The effective focal length (\(f^{\prime}\)), the diameter of entrance pupil (\(D_{p}\)), the diameter of aperture stop (\(D_{a}\)) and objective distance (\(d_{o}\)) are 3.21 mm, 0.35 mm, 0.836 mm and 2 mm, respectively. The phase mask will be placed at the aperture stop when aiming at extending the depth of field (DOF). Figure 3: Simulated MTF curves of initial optical endoscope varied with object distances. (**a**) Tangential direction. (**b**) Sagittal direction. The blue line, green line, orange line, yellow line, mazarine line, purple line, cyan line, aquamarine line and gray line represent the object distances of 1.8 mm, 2.1 mm, 2.4 mm, 2.7 mm, 3 mm, 3.4 mm, 3.8 mm, 4.2 mm and 4.5 mm, respectively. Figure 2: Calculated modulation transfer function (MTF) curves of initial optical endoscope (from Equation (9)) varied with object distances. The blue dotted-dashed line, green dashed line, red thin line, blue dotted line and purple thick line represent the object distances of 2 mm, 2.5 mm, 3.2 mm, 3.9 mm and 4.6 mm. The normalization factor, also referred as maximum spatial frequency, depends on the diameter of Airy disk of the optical system, which can be defined as \(\frac{D_{s}}{4\times 1.22\times d_{o}\times\lambda}\). According to the parameters of diameter of entrance pupil \(D_{a}\) = 0.836 mm, objective distance \(d_{o}\) = 2 mm and \(\lambda\) = 0.586 μm, the calculated normalization factor is 146 lp/mm.
# Microgravity, Bone Homeostasis, and Insulin-Like Growth Factor-1
## 4 Mechanotransduction in Space (\(1\times 10^{-6}\) g)
### Osteoblasts  
A site-specific bone loss of 1-2% occurs in astronauts and in-flight animals after 1 month of spaceflight. The loss is associated with changes in osteoblast cellular and nuclear morphology and altered mRNA expression of type I collagen, osteoclastcin, and insulin-like growth binding proteins [20]. [PERSON] and associates found that osteoblasts subjected to 5 days of microgravity had extended shapes, fragmented or condensed nuclei, shorter microtubules, and smaller and fewer focal adhesions [21].  
[PERSON] found that serum-activated osteoblasts had a 60% reduction in growth as compared to ground controls, presumably due to alterations in growth factor receptors and/or cell-connected matrix kinase pathways. After 4 days in space there was a loss of focal adhesions and a complete collapse of the osteoblast cytoskeleton. He postulated that the collapse was due to F-actin microfilament failure and/or inhibition of microtubular polymerization [22]. The same investigator had previously reported that cultured osteoblasts had increased levels of C-fos mRNA and diminished levels of Cox-2 mRNA [23]. C-fos is a protooncogene that encodes a 62 kDa protein that forms a heterodimer with c-jun (part of the jun family of transcription factors) resulting in the formation of activator protein 1 (AP-1) complex which binds DNA at AP-1 specific sites at the promoter and enhancer regions of target genes and converts extracellular signals into signals of gene expression. Cox-2 catalyzes the rate-limiting step in prostaglandin synthesis. Using cultures of ROS 17/2.8 osteoblasts, [PERSON] and associates reported that osteoblastic integrin-mediated cell adhesion was disrupted in cells flown for six days on the space shuttle. The changes were characterized by disorganization of the cytoskeleton associated with disassembling of vinculin spots and phosphorylated proteins within the focal contacts [24]
# Fusion of Bilateral 2 DPCA Information for Image Reconstruction and Recognition
## 3 Methodology
### Relationships between DB2 DPCA and Other Algorithms  
An image is essentially a sample containing \(d(=\)\(h\times w\)) features after neglecting its 2D structure. To investigate the relationships between DB2 DPCA and related algorithms, we reshape images into \(p\times q(\geq\)\(d)\) matrices by the same rule. Three extreme conditions are stated below.  
(1) When \(p=1\) and \(q=d\), we can obtain that \(\mathbf{U}\) equals the projection matrix of conventional PCA and \(\mathbf{V}=1\). In this case, the feature matrix for a test sample \(\mathbf{Y}\) is calculated by concatenating two parts, wherein one part is the feature matrix calculated by PCA and the other part is the result of subtracting the mean image from the sample image. The reconstruction image of \(\mathbf{Y}\) is calculated by averaging the reconstruction image of PCA and the sample image.  
(2) When \(p=h\), \(q=w\) and the pixels in images are not reordered, the projection and reconstruction results of DB2 DPCA are just integrating the results of 2 DPCA and A2 DPCA, as shown in Equations (16) and (17), respectively.  
(3) When \(p=d\) and \(q=1\), we can obtain that \(\mathbf{U}=1\), and \(\mathbf{V}\) equals the projection matrix of conventional PCA. In this case, the projection and reconstruction results of DB2 DPCA are the same as the results in case of \(p=1\) and \(q=d\). This shows the symmetry property of DB2 DPCA.  
In general, \(p\) could be an arbitrary integer satisfying \(1\leq p\leq d\). Thus, \(q=\left[d/p\right]\)--namely, \(q\)--is the smallest integer no less than \(d/p\). The symmetry of DB2 DPCA indicates that DB2 DPCA on images of size \(p\times q\) approximates DB2 DPCA on images of size \(q\times p\)By taking this symmetric property into consideration, we only need to change \(p\) in the range of \([1,\sqrt{d}]\) in order to investigate its effect on the performance of DB2 DPCA.  
Since \(p\times q\geq d\), there may exist blank pixels that should be filled in a reshaped image. Some potential choices are randomly picking some pixels from the original image to fill the blanks, calculating the mean value of all pixels in the original image to fill the blanks and filling the blanks with the nearest non-blank pixels. If only the cases when \(d\) is divisible by \(p\) are considered, we can avoid the noises introduced by these operations.
## 4 Results and Discussion
### Evaluate the Influence of Main Factors  
Figure 5(a),b show the apparent influence of each factor on the compressive and flexural strength of cement mortar. This figure revealed that there had been a sharp increase in strength from 3 days to 7 days, especially 28 days later.  
Considering the influence of fly ash, both graphs show a slight change. The strength of mortar tends to rise when the FA used changes from 0 to 15 wt%. However, at 30 wt% usage, both compressive and flexural strength decreases, and the graph slopes sharply downward.  
Meanwhile, when the proportion of cement weight grows, the amount of GO used remains very tiny, ranging from 0.01 wt% to 0.02 wt%. It leads to an increase in strength, with the flexural strength curve being steeper than the compressive strength curve. This trend continues to increase sharply (very steep graph) when GO up to 0.03 wt% is used. It is also consistent with other studies in the world [12; 22; 23; 24; 25]. However, with 0.04 wt% and 0.05 wt% of GO dosages, the compressive and flexural strengths both decrease very clearly. This decrease when the crystals of the cement hydrate can explain the increase in the content of GO agglomerate. While the presence of PCE and FA in the mixture aids in the dispersion of cement particles and the release of trapped water in fl  
Figure 5: The relationship between FA and GO content and workability.  
Figure 6: The main factors affecting this are (**a**) compressive strength and (**b**) flexural strength.  
essential to ensure that the level of GO does not exceed 0.04 wt%. According to research by some authors, GO content should be in the range of 0.02 wt%-0.01 wt% [24], and some other studies have found that GO use up to 0.08% is still reasonable for compressive strength, but some other properties tend to decrease, as researched by [6].  
The interaction plots in Figure 7 show all of the graphs of the samples with segments with different slopes compared to the graph of the standard sample. Hence, the interaction effect between the variables in each cell was significant. However, the assessment based on observing the graphs gives quick qualitative results. To have a more specific conclusion, it is necessary to consider the quantitative characteristics below.
# Enhanced Curvature-Based Fabric Defect Detection:
## 5 Ablation Study on Deep Learning-Based Defect Detection
The number of epochs was determined experimentally, ensuring that the model had enough time to converge without training for too long, which could lead to diminishing returns and overfitting. The model accuracy over 20 epochs is presented in Figure 17a. During training, the train loss, representing the error in the training data, steadily decreased, indicating improved performance. The val loss, measuring the error on unseen validation data, was carefully monitored to prevent overfitting and ensure the model generalized well to new data. The changes in train loss and val loss over the course of training can be observed in Figure 17b. For optimization, the Adam optimizer was selected due to its adaptive learning rate capabilities and overall efficiency in training deep learning models, as described by [52]. Adam is known for its ability to converge quickly by adjusting the learning rate for each parameter dynamically, which makes it well-suited for complex models like the one used in this study. Additionally, the categorical cross-entropy loss function was used, as this is the standard loss function for multi-class classification problems [53]. It calculates the difference between the true labels and the predicted probabilities output by the model, and minimizing this difference helps the model improve its predictions. Overall, these hyperparameters were chosen based on their effectiveness in training deep-learning models and were fine-tuned to maximize performance in fabric defect detection. The pseudo code of the described Deep Learning Based Algorithm is presented Algorithm 3.
# Boundary-Aware Transformer for Optic Cup and Disc Segmentation in Fundus Images
## 7 Limitation and Future Work  
In this study, independent training and evaluation were conducted on five publicly available fundus image datasets--DRIONS-DB, Drishti-GS, REFUGE, G1020, and ORIGA. By utilizing multiple datasets that reflect a variety of imaging devices, resolutions, and patient characteristics, we aimed to objectively validate the generalization capability of the proposed model.  
Although our research team possesses separately collected hospital-based private clinical data, they were not directly utilized in this study for the following reasons: (1) the absence of high-quality annotations for OD and OC boundaries, (2) ethical restrictions, including compliance with personal data protection laws, and (3) an insufficient number and distribution of patients across age groups and genders, which limited the feasibility of statistical analysis. Due to these constraints, all experiments were conducted using publicly available datasets. However, in real-world clinical settings, models are required to generalize across various factors such as ethnicity, gender, age, and imaging conditions. In future work, we plan to adopt patient-specific fine-tuning strategies and condition-aware learning techniques to further expand the applicability of the model to diverse patient populations and imaging scenarios.  
Although this study focused on OD and OC structure segmentation in fundus images, building a complete system for early glaucoma diagnosis will require the integration of additional downstream tasks, such as Cup-to-Disc Ratio computation, disease risk assessment, and longitudinal monitoring. In future research, we aim to develop a multi-task learning framework based on the proposed segmentation model to facilitate the integrated analysis of quantitative biomarkers and clinical diagnosis.  
Additionally, the proposed model currently faces limitations under extreme conditions, such as severe image quality degradation or the presence of rare or atypical structural abnormalities. Future work will focus on improving the architecture and incorporating hard-case-focused learning strategies to enhance robustness in such challenging scenarios.  
Finally, while the current study designed an architecture optimized for fundus imaging, the generalizability of the proposed method to other medical imaging modalities, such as Optical Coherence Tomography (OCT), has not yet been explored. Given that Boundary-aware Transformer Attention and Geometry-aware Loss are well suited for tasks requiring precise boundary recognition, future work will aim to expand the model to different imaging modalities and strengthen its universality.
# State of the Art in LP-WAN Solutions for Industrial IoT Services
## 3 LP-WAN Solutions for IIoT Services
### Standardization Bodies' Efforts
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**LoRaWN**} & \multicolumn{2}{c}{**Sigfox**} & \multicolumn{2}{c}{**Weightless**} & \multicolumn{2}{c}{**Ingenu**} & \multicolumn{2}{c}{**Telemas**} & \multicolumn{2}{c}{**Dash7**} & \multicolumn{2}{c}{**Telem820.15A**} & \multicolumn{2}{c}{**IEEE**} & \multicolumn{2}{c}{**UESSESS**} & \multicolumn{2}{c}{**P802.11 ah**} & \multicolumn{2}{c}{**LTE-MTC**} \\ \cline{3-14} \multicolumn{1}{c}{Band} & 433/686/780/915 MHz & 868/915 MHz & TV whitepaper & Sub-GHz & Sub-GHz & \multicolumn{2}{c}{24 GHz} & Sub-GHz & Sub-GHz & \multicolumn{2}{c}{Sub-GHz} & \multicolumn{2}{c}{Sub-GHz} & \multicolumn{2}{c}{Collular} \\ \hline Max. data-state & 50 kbps & 100 bps & 10 Mbps & 100 kbps & 100 kbps & 19 kbps/MHz & 346 Mbps & - & 125 kbps & 346 Mbps & 200 kbps \\ \hline Range (urban) & 5 km & 10 km & 5 km & 5 km & 2 km & 15 km & 1 km & 3 km & 5 km & 1 km & 5 km \\ \hline Packet-size & Max. 25 s & 12 s & Min. 10 b & Max. 20 b & Min. 10 b & Max. 10 b & Max. 63 sH & - & Max. 32 s & Max. 63 sH & - \\ \hline Downlink & Yes. Different plans & Yes (not sym.) & Yes (sym.) & No & Yes (sym.) & Yes (not sym.) & Yes (sym.) & Yes (sym.) & Yes (sym.) & Yes (sym.) & Yes (sym.) & Yes (sym.) \\ \hline Topology & Star-of-stars & Star & Star & Star & Star/Tree & Star/Tree & Star & Star & Star/Tree & Star \\ \hline Roaming & Yes & Yes & Yes & Yes & Yes & Yes & Yes & Yes & - & Yes & Yes \\ \hline Security & Fully addressed & Partially & Fully & Fully & Fully & Fully & In & - & Partially & In & In \\  & & addressed & addressed & addressed & addressed & addressed & development & development & development & development \\ \hline Protocol & Partially proprietary & Proprietary & Standard & Standard & Standard & Proprietary & Standard & Proprietary & Standard & Standard & Standard & Standard \\ \hline \hline \end{tabular}
\end{table} 
Table 1: LP-WAN platforms summary.