esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Set Up and Optimization of the Catalyst Deposition Process
It was found that a lower pH was favorable for the deposition process, with a mean weight of 3.6 mg (+29% vs. 50 \({}^{\circ}\)C @pH7) for HCl and 4.0 mg (+43% vs. 50 \({}^{\circ}\)C @pH7) for HNO\({}_{3}\). However, this second set of samples showed a rough surface and lumpy areas, and a moderate increase in weight with respect to the samples treated with HCl and a significant loss of homogeneity of the surface was observed. Conversely, the deposition carried out at pH 12 via the addition of ammonia led to a modest increase in the deposited layer, equal to +11% or 3.1 mg in total. This is in line with the already discussed effect of pH on the surface charge density of particles. At pH \(>\) 8, the surfaces of both glass and titania particles are negatively charged, disfavoring a surface interaction, at least of electrostatic nature [29; 33; 34]. In addition, the surface was even less homogeneous; thus, the best option was represented by the addition of HCl and the dip coating performed at 50 \({}^{\circ}\)C. As expected, many subsequent cycles of deposition led to a remarkable increase in the overall weight of the titania layer, up to 10.7 mg after six treatments (Table S4, see Supporting Information File). So far, 5% \(w/w\) has represented the maximum TiO\({}_{2}\) concentration to avoid the massive precipitation of titania in the absence of stirring. To try to overcome this limit, thus improving the amount of titania loading per cycle, we tested an industrial emulsifying agent for paints based on titanium dioxide named Disperbyk-190\({}^{\text{\textregistered}}\). This is an organic-based compound that stabilizes the water suspension of titanium dioxide when added up to 3%\(w/w_{\rm TiO2}\). By using Disperbyk-190\({}^{\rm\tiny\textregistered}\), it was possible to increase the concentration of the suspension up to 25% \(w/w\) without significant precipitation. Different suspension concentrations were tested, and Table 1 summarizes the results achieved using P25 suspensions in the range of 5-25% \(w/w\). At ambient temperatures and after two deposition cycles, the average weight of the deposited layer was almost 30% higher than our benchmark at 5% \(w/w\), but still 1.5 times lower than the weight obtained using HCl at 50 \({}^{\circ}\)C. However, thanks to the additive, it was possible to match the performance of the latter setup simply by increasing the amount of titania added to the water to 10% \(w/w\). In addition, when this ratio was set to 15.5% the average weight of the P25 deposited over the glasses increased three-fold (12.6 mg vs. 4.3 mg). Lastly, by pushing the system to the limit with a 25% solution of P25 in water, it was possible to obtain a very thick deposited layer, equal to 100 mg per tile, that, although very homogeneous, was quite fluffy and easy to peel off if accidentally touched. In light of these results, we concluded by performing just one impregnation with a suspension of 15% (\(w/w\)) to ensure proper adhesion to the glass surface.
# FFG-YOLO: Improved YOLOv8 for Target Detection of Lightweight Unmanned Aerial Vehicles
## 4 Results
### Visualization of Test Results  
To evaluate each model's detection ability, we visualized and compared their results on the same image (Figure 5), revealing differences in target localization accuracy, bounding box fitting quality, and small-target perception. FFG-YOLO demonstrated superior performance: its predicted bounding boxes aligned accurately with ground truth, enabling precise separation of dense objects. It particularly excelled in small-target detection and occluded regions, showing strong boundary consistency and target integrity. This enhanced foreground perception, local structure modeling, and feature discriminability were attributed to the FCB, FEB, and GCAB modules.  
In contrast, YOLOv8n, YOLOv10n, and YOLOv11n were relatively stable for large target recognition but suffered from bounding box drifts, false positives, and missed detections for small, long-distance targets or in complex backgrounds, reflecting deficiencies in feature representation and spatial detail modeling. RetinaNet and RT-DETR showed higher localization accuracy for large targets but lacked sufficient discriminative capability in dense scenes. Despite RT-DETR's global modeling capacity, its ineffective local feature sensing often led to frequent missed detections and boundary regression errors in images with numerous objects.  
Training convergence was also compared, with mAP@50 curves for FFG-YOLO, YOLOv8, YOLOv10, YOLOv11, RT-DETR, and FCOS presented in Figure 6. These curves illustrate each model's optimization speed and accuracy. FFG-YOLO and other YOLO-series models exhibited fast convergence, with mAP@50 rapidly increasing in early iterations and stabilizing later. This indicates strong optimization ability and high detection accuracy from an early stage. Though YOLOv10 and YOLOv11 showed minor fluctuations in later stages, their mAP@50 remained stable, achieving high accuracies that reflect robust feature representation and target localization. In contrast, RT-DETR showed a slower initial mAP@50  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Module Combination** & **mAP@50 (\%)** & **mAP@50–95 (\%)** & **Recall (\%)** \\ \hline
**YOLOv8n** & 34.6 & 19.7 & 35.1 \\
**YOLOv8n + WDLoss** & 34.62 & 20 & 34.1 \\
**YOLOv8n + FFG** & 34.61 & 20.3 & 34.9 \\
**YOLOv8n + WDLoss + FFG** & 34.7 & 20.1 & 34.5 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Ablation experiment results and comparison.
# Towards an Efficient Method for Large-Scale Wi-SUN-Enabled AMI Network Planning
## 2 Smart Grid Network Architecture  
The two main components of a smart grid communication network are the AMI network and the automation network. In both networks, we must establish two-way communication with the control center for data acquisition and management purposes. Using a standardized wireless communication architecture, the AMI network connects smart meters, routers, and gateways. The automation network is mission-critical because it connects DA devices to the smart grid communication infrastructure. The automation network architecture is highly dependent on the correct positioning of the key devices.
# Natural Photosensitizers in Clinical Trials
## 5 Reported Toxicity and Adverse Side Effects of Natural Photosensitizers  
No serious side effects have been described in clinical studies of CUR-PDT. No negative effects of curcumin on healthy tissues have been reported in them [35; 36; 37; 38; 39; 40; 41]. While treating acne, [PERSON] et al. described a mild warm sensation accompanying CUR-PDT. They saw mild erythema on both sides, which resolved by 2 h. Two patients experienced mild pigment deposition on both cheeks after the two treatments in both the experimental and control groups. This pigmentation resolved spontaneously after a 1-month follow-up [41]. In two of the three clinical studies describing hypericin as a photosensizer, side effects were reported, indicating the relevance of the therapeutic regimen used in causing them. In the study by [PERSON] et al., the side effects observed were mild local skin lesions and injection site reactions. No serious adverse events related to the drug occurred [54]. In the study by [PERSON] et al., all patients complained of burning and pain during irradiation. No erythema or swelling was observed after treatment, and no other serious side effects were reported [55]. In the papers in which riboflavin was studied, no adverse effects were described [63; 64; 65]. [PERSON] et al. reported no adverse effects of PDT with phyocyanin. This PS did not cause discoloration of healthy tissue or have toxic effects on healthy dentin [74]. In a clinical trial, nano-emodin showed no negative side effects. Moreover, PDT with this PS can reduce inflammation and promote tissue healing and reduce postoperative pain [82]. We did not locate any study describing the safety and toxicity of using psoralen in photodynamic therapy, but PUFA therapy using this compound and UVA light can cause side effects such as discomfort, skin disease, and even melanoma [138; 139]. In vivo studies determining the toxicity of PDT with pheophorbide a indicate the overall safety of this photosensitizer. [PERSON] et al. observed no significant side effects of PDT with this PS on mice: PDT was well tolerated, and there was no increase in liver enzymes or cardiac LDH levels [96]. [PERSON] et al. also found no systemic toxicity in a mouse study during treatment of oral squamous cell carcinoma [98]. [PERSON] et al. reported no serious side effects in mice during Pa-PDT treatment of breast cancer [97]. However, studies precisely verifying toxicity to healthy cells are conflicting. Normal human liver cells (WRL-68) showed low sensitivity to Pa-PDT, with cell viability remaining at 85% [96]. On the other hand, in four primary cultures of normal mammary epithelial cells, Pa-PDT showed a similar cytotoxic effect to cancer cells [97]. Pa-PDT was also found to be toxic to neutrophils [140]. We have not located any in vivo study evaluating the toxicity and
# A Magnetotelluric Signal Acquisition and Monitoring System Based on a Cloud Platform
## 5 Development of Cloud Platform Monitoring Software
### Data Communication Development
Thread n first verifies the instrument's identity and permissions (e.g., IP, key) to ensure that the connection is legal. The heartbeat thread periodically sends lightweight heartbeat instructions to the instrument to confirm its online status. In this study, the heartbeat instruction is a request for the basic information of the instrument. The instrument responds immediately upon receiving the request. If no response is received multiple times, the instrument is judged to be disconnected, triggering a reconnection or alarm mechanism. The request thread sends instructions at preset intervals, and the instrument returns real-time data. After receiving the instrument's data, the format and logic of the returned data need to be checked. Valid data is recorded in the database or log, forming a traceable historical record. Figure 20: Program logic of the cloud platform’s Socket communication. The thread enters a waiting state after sending instructions. If no response is received within the timeout period, it retries or marks an exception. If the connection is interrupted, the thread attempts to re-establish the Socket connection to ensure service continuity. When the instrument actively disconnects or does not respond for a long time, the corresponding Socket resources and thread are released to avoid resource leaks.
## 7 SINR Ordering for MBMF-SIC  
For conventional sequential ICI cancellation for OFDM systems, the cancellation is performed on a subcarrier by subcarrier basis, because the previous soft symbol estimates \([\hat{s}_{k-2D},\ldots,\hat{s}_{k-1}]\) inside the band are needed in the cancellation for the desired subcarrier \(s_{k}\). In this case, there is no significant benefit from the ordering, due to the absence of previous soft symbol estimates. With the iterative ICI cancellation and the channel decoder, the soft symbol estimates \([\hat{s}_{k-2D},\ldots,\hat{s}_{k-1}]\) are known beyond the initial iteration, so any ordering can be performed. However, its performance improvement by ordering is very small. On the other hand, the sequential ICI cancellation will introduce the aggregation of residual interference. For MBMF-SIC, the interference is reconstructed by the multi-feedback symbols vectors \([s_{k-2D}^{b},\ldots,s_{k-1}^{b}],b=1,2,\ldots,B\), so the aggregation of residual interference from previous soft symbols can be significantly removed. Furthermore, we assume that the conditional probabilities of \(\mathbf{y}_{k}^{(b)}\) given \(\mathbf{s}_{k}^{(b)}\) and \(\mathbf{\tilde{y}}_{k}\) given \(\mathbf{\hat{s}}_{\bar{k}}\) are conditionally independent in Equation (15). This will be true if the residual interference inside the band in Equations (10) and (12) dominates. This assumption may be more reasonable by the ordering, which can reduce the coupling effects brought by the previous soft symbol estimates. Following Equations (10) and (30), the SINR of the \(k\)-th subcarrier for ordering can be evaluated as similar to the SINR ordering for the conventional SIC.  
\[\
u_{k}\approx\frac{(|\mathbf{h}_{k}^{H}\mathbf{h}_{k}|)^{2}E_{s}}{E_{s} \text{tr}(\mathbb{E}\{\mathbf{h}_{k}\mathbf{h}_{k}^{H}\}\mathbb{E}\{\mathbf{c }_{k}\mathbf{c}_{k}^{H}\})+\sigma_{\bar{v}}^{2}+\text{tr}(\sum_{k=k-Q,q\
eq k} ^{k+Q}\mathbf{h}_{\bar{k}}\upsilon_{k}\mathbf{h}_{\bar{k}}^{H})}, \tag{54}\]  
where \(\upsilon_{k}=\mathbb{E}\{(s_{q}-\hat{s}_{q})(s_{q}-\hat{s}_{q})^{*}\}\). In the remaining steps, the quantity \(\
u_{k}\) can be sorted for \(k=1,\ldots,N_{s}\), and the detection ordering can be implemented accordingly. Other ordering methods can also be employed on the basis of the error probability or LLRs, the calculation of which will introduce the channel matrix inversion, with complexity at least \(\mathcal{O}((2D+1)^{2})\). Hence, the complexity of LLR ordering in [33] will be more complicated than that of SINR ordering.
## 2 The Proposed Method
### Invertible Denoising Network
#### 2.2.2 Invertible Block  
In each DownScale Block, the input image is decomposed into high-frequency and low-frequency signals through the wavelet transform layer. The received signals are subsequently subjected to processing through a sequence of invertible blocks. Specifically, the InvDN framework employs coupling layers [14] as its invertible blocks. Assuming input \(u^{i}\) and output \(u^{i+1}\), the R1-R4 operations in forward propagation are as follows: \(u^{i}_{a},u^{i}_{b}=Split(u^{i})\), \(u^{i+1}_{a}=u^{i}_{a}\odot exp\big{(}\phi_{1}\big{(}u^{i}_{b}\big{)}\big{)}+ \phi_{2}\big{(}u^{i}_{b}\big{)}\), and \(u^{i+1}=\)_Concat_\(\big{(}u^{i+1}_{a},u^{i+1}_{b}\big{)}\); the R1-R4 operations in backward propagation are as follows: \(u^{i+1}_{a},u^{i+1}_{b}=Split(u^{i+1})\), \(u^{i}_{b}=(u^{i+1}_{b}-\phi_{4}\big{(}u^{i+1}_{a}\big{)})/exp(\phi_{3}\big{(}u ^{i+1}_{a}\big{)})\), and \(u^{i}_{a}=(u^{i+1}_{a}-\phi_{2}\big{(}u^{i}_{b}\big{)})/exp(\phi_{1}\big{(}u^{ i}_{b}\big{)})\). The \(Split(.)\) operation denotes the channel-wise splitting of the input. \(\phi_{1},\phi_{2},\phi_{3},\phi_{4}\) can be any neural network, \(\odot\) denotes the multiplication operation, while/denotes the division operation. The \(Concat(.)\) operation denotes the channel-wise merging of the input.  
Figure 2: InvDN structure.  
Among these operations, the split operation divides the input image \(u^{i}\) of size (H/2, W/2, 4C) into \(u^{i}_{a}\) and \(u^{i}_{b}\), representing the low-frequency signal of size (H/2, W/2, C) and the high-frequency signal of size (H/2, W/2, 3C), respectively, capturing image texture and noise. The backpropagation in rows R2 and R3 only requires the inversion of addition and multiplication operations to subtraction and division, while the network's operations do not necessitate invertibility. Therefore, any network can be utilized for \(\phi_{1},\phi_{2},\phi_{3},\phi_{4}\). In this study, we employ residual blocks [15] for accomplishing \(\phi_{1}\), \(\phi_{2}\), \(\phi_{3}\), \(\phi_{4}\). Considering the significance of skip connections in deep denoising networks [16; 17], we can simplify the forward propagation component in R2 as follows:  
\[\mathrm{u}^{\mathrm{i+1}}_{\mathrm{a}}=\mathrm{u}^{\mathrm{i}}_{\mathrm{a}}+ \phi_{2}\Big{(}\mathrm{u}^{\mathrm{i}}_{\mathrm{b}}\Big{)} \tag{7}\]  
The Concatenation operation serves as the inverse of the Split operation, effectively merging feature maps along the channel dimension and subsequently transmitting them to the subsequent module.
## 4 Conclusions  
In this paper, a simple, low-cost, narrow linewidth, single longitudinal mode fiber ring laser is designed. The effect of different lengths of EDF on the laser performance is investigated. Using a 12 m EDF, 59.86 dB of OSNR is obtained. The central wavelength is 1551 nm, and the maximum output power is 2.8 mW. To optimize the laser linewidth, the feedback of RBS is tested by adjusting the SMF length of the RBS cavity. We find that a 120 m SMF inside the RBS cavity has the best laser performance. The single longitudinal mode output can be achieved by adjusting the VOA with a laser line width of 1.46 kHz and laser power of 9.4 mW. In a receiving power of \(-16.2\) dBm, the BER performance is \(10^{-9}\) at 10 Gb/s NRZ modulation speed. Future work will design a thermally controlled methodology and package the fiber laser to isolate it from the environmental conditions.  
Figure 8: Experiment setup: PC: polarization Controller; EOM: electro-optic modulator; EDFA: erbium-doped fiber amplifier; VOA: variable optical attenuator; PRBS: pseudo random binary sequence; OSA: optical spectrum analyzer; PD: photodetector; BERT: bit error rate tester; BPF: band-pass filter; DPO: digital phosphor oscilloscope; PM: power meter.  
Figure 9: (**a**) OSNR of fiber laser before and after the EDFA. (**b**)BER performance of the proposed extra narrow linewidth fiber laser.  
**Author Contributions:** Methodology, Z.W.; validation, H.-H.T.; Experiment, Z.W., G.-Y.C.; data curation, L.-E.W.; Preparation, D.-C.L.; writing--original draft, D.-C.L., S.-K.L.; review and editing, C.-H.Y., Y.-L.Y.; Supervision, S.-K.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Ministry of Science and Technology, Taiwan, grant number 110-2224-E-011-004 and 110-2221-E-011-109. And bilateral project of TMU and NTUST grant number TMU-NTUST-111-01.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Acknowledgments:** [PERSON]'s and [PERSON]'s help are much appreciated.
## 1 Introduction  
Infrared small target detection technology is critical in applications such as infrared early warning [1], buried land detection [2], and remote sensing-based aircraft detection [3]. However, infrared imaging is typically conducted from long distances, resulting in small, dim targets that lack distinct texture features. These faint targets are often obscured by complex background clutter and noise, making them difficult to detect. Additionally, the irregular motion and shapes of infrared small targets complicate their dynamic characteristics. Extensive practical experience has demonstrated that relying solely on temporal or spatial information hampers effective detection. Consequently, the effective utilization of spatiotemporal information for robustly detecting infrared small targets in complex backgrounds remains a prominent research focus among experts and scholars
# Order-Based Identification of Bearing Defects under Variable Speed Condition
## 3 Validation of the Proposed Features
### Experimental Setup  
Referring to [14], the experiments are carried out on the bearing fault simulator test bench (MFS-PK5M) presented in Figure 1.  
It consists of a rotating shaft driven by an AC drive-controlled motor and supported by two ball bearings type ER16K. The motor-side bearing is considered to be healthy. The right-side bearing is considered to be the experimental one, replaced by bearings of different health states. Vibrations are acquired using an ICP accelerometer, model 623C01 placed in the housing of the experimental bearing radially in the vertical direction as shown in Figure 1. The shaft rotation speed is acquired using an EPC 775 incremental encoder. The data considered correspond to an experimental bearing with: (i) healthy state, (ii) localized inner race defect, (iii) localized outer race defect, (iv) localized ball defect, and (v) combined defects on the inner race, the outer race and the ball, operating under (i) speed increase, (ii) speed decrease, (iii) speed increase and then decrease, and (iv) speed decrease then increase conditions.  
In total, 60 vibration signals sampled at 200,000 Hz with a duration of 10 s each are considered in this study, i.e., 12 data sets for each health condition (4 signals are available  
Figure 1: The experimental setup. Reproduced with permission from [14], Elsevier, 2021.  
for each operating speed condition). Signals can be found in [14]. A detailed description of the acquired signals is available in [14].  
Table 1 summarizes the characteristics of the bearings used in the experiments and gives the characteristic frequencies \(f_{o}\), \(f_{i}\) and \(f_{b}\) as a function of the shaft rotation frequency \(f_{r}\).
## 3 Results
### Spatial Resolution with the GEMPix  
In order to evaluate the contribution of the isotropic emission of the scintillation light to the spatial resolution in the LaGEMPix, we carried out comparative measurements with the GEMPix, a 2.8 \(\times\) 2.8 cm\({}^{2}\) triple-GEM detector that uses a Timepix quad ASIC to readout charges directly [6,54]. The experimental conditions were identical: 40 kV X-rays (N-6 series filter), minimum aperture 1 cm, 230 cm source-detector distance and same electric fields in the GEM structure.
# Optimization Techniques for Design Problems in Selected Areas in WSNs: A Tutorial
## Using Mobile Radio Frequency (RF) Power Charger to Charge the Batteries of Sensors in a WSN
### Observations and Comments on the Problem Formulation, Solution Method and Experiments  
The following are our comments on on the problem formulation in [9]  
1. We believe that the constraint given in Equation (33) is not accurate because it accounts only for the energy expended in one location. A mobile charger can visit different landmarks, therefore, to guarantee that its total supply is not violated, then the summation should be done over all the possible landmark positions (x,y), i.e., \[\sum_{x}\sum_{y}\sum_{i}z^{i}_{xy}\delta_{i}\leq\tau_{i}\] (34) This will also give an advantage of reducing the number of constraints for this set from \(x\times y\) constraints to only one constraint.
2. The algorithm type and its details that solves the ILP was not mentioned. Choosing a suitable algorithm for e.g., branch and bound (BnB), and experimenting different branching methods, different relaxations to bound the subproblems for each node in the BnB tree, different methods of node selection and maybe integrating cutting planes (that could be problem specific) can greatly reduce the storage requirements and computational effort.
3. The positions of the land marks (x,y) are discrete in this formulation, however in practice they are continuous. The discretization could be an acceptable technique but the resolution definitely has an impact on the solution and the required computational effort. A high resolution gives the most accurate solution but leads to a large problem size requiring high storage and computational effort. Low resolution is vice versa.
4. The results illustrated in [9] compared the system performance mainly in terms of received power by the sensor nodes for DRIFT and the older proposed reference scheme SuReSense. No numerical results were provided to show the required computational effort and/or time of the proposed DRIFT ILP program and no information regarding the discretization of the landmark position was provided. We believe that some numerical experiments need to be done for different resolutions and the most suitable resolution in terms of the objective function value and the resulting problems size could be selected. For a mobile wireless charger, it is expected that the storage and computational capabilities to be very limited and hence should be taken into consideration when selecting a particular resolution.
5. It is expected for large WSNs, to have more than one mobile wireless charger, therefore we believe that it would be useful if the problem is extended to propose a distributed algorithm that balances the computational effort of solving the problem across the different mobile wireless chargers.