esa-sceva/satcom-chunk-collection huggingface dataset:
# Efficient Resource Scheduling for Multipath Retransmission over Industrial WSAN Systems
## 3 Network Model  
### Network Description  
Deterministic communication heavily relies on resource scheduling management. Accordingly, network management techniques adapted for industrial wireless mesh are critical. This paper adopts centralized management to form a deterministic WSAN, the structure of which is shown in Figure 1.  
A network mainly consists of field nodes, multiple sinks, a gateway, and a centralized network manager. All field nodes operate over low-power radios compliant with the IEEE 802.15.4-2006 standard, which supports 16 channels in the 2.4 GHz ISM band. The TSCH MAC protocol is implemented in this paper to guarantee deterministic communication. One time slot is 10 ms long and allows for channel switching and the transmission of a single packet together with data-link acknowledgement (DL-ACK). Time slots can be either dedicated or shared, where the shared time slot is amended to CCA-Embedded Slot (CES) as proposed in Reference [34]. In a dedicated time slot (DTS), only a single transmitter-receiver pair can communicate on any given channel, thereby not allowing channel reuse. CES, in which multiple nodes are scheduled on the same channel, is mainly used for decreasing the use of slots for path crossing.  
To guarantee the reliability of the flow transmission, we adopt the graph routing retransmission approach advised in the WirelessHART standard. As shown in Figure 2, packets can be sent by a device which has two direct neighbors at least in a graph. One is the primary routing path (PRP), and another is the alternative routing path (ARP). Each type of path has a receiving node for relaying packets. A node should preferentially send packets along the PRP. If the transmission is failed in PRP, the node will change to using ARP. In this paper, we assume that the multipath graph is already generated by the network manager. Therefore, all the possible transmissions should be involved in a slot and channel scheduling. The scheduling of slot and channel for all packet flows also has some considerations and limitations in the following based on WirelessHART.  
* The allocation of slot and channel based on graph routing is so that a device can send a packet at most three times, including the first transmission and two retransmissions. The first transmission and retransmission are along the PRP, and the second retransmission needs to change to be along the ARP, where the changes of link includes the neighbor and channel.
* According to the above consideration, each flow consists of dedicated and abundant transmissions, all which should be allocated for the slots and channels by the network manager. Moreover, each  
Figure 1: The structure of an industrial wireless sensor-actuator network (WSAN) system.  
Figure 2: An example of the graph routing retransmission scheme.  
slot allocation should follow the routing orders, where the slot offset of next-hop transmission and retransmissions should not be allocated before the slots of the previous-hop transmission.
## 7 Conclusions
Finally, as emerged from this work: (1) The results of this study show that Sentinel-1 and -2 data are complementary approaches to detect water level variations across time and hence to support a sustainable urban management and planning; (2) the use of other radar historical ESA archives (i.e., ERS and ENVISAT satellites) dated back to 1992 and of optical data available since the 1940s by exploiting satellite and aerial imagery allow researchers to perform a \"back analysis\" to improve the knowledge on the historically more prone to inundation areas; (3) the proposed satellite elaborations, if supported by a multidisciplinary territorial analysis, can help to understand flood/inundation hazard both from a broad point of view to a detailed point of view, given the high quality of the produced data; (4) we believe that the presented methodology could be easily applied by the local administrations to map the water surface and subsequent to prevent and manage flood in the inherited territorial context and to assess the flood vulnerability that has to be included in the city and regional master plan vulnerability assessments. Conceptualization, S.M. ; methodology, S.M., M.D.S. and S.B. ; software, M.D.S., S.B. ; validation, E.K., E.S. ; formal analysis, S.M. ; investigation, S.M., M.D.S. and S.B. ; resources, E.K., E.S. and N.C.; data curation, S.M., M.D.S., S.B. and V.P. ; writing--original draft preparation, S.M., M.D.S., S.B., V.P. and E.K. ; visualization, V.P. ; supervision, S.M., N.C.; project administration, N.C. This research received no external funding. **Acknowledgments:** The authors would like to thank the local authorities and University of Shkoder for the provided support in data collection and in the technical suggestions about the real needs of the local community in relation to flood prevention. **Conflicts of Interest:** The authors declare no conflict of interest.
# RSSI-Based MAC-Layer Spoofing Detection: Deep Learning Approach +
## 5 Experiments and Results
### Note on Special Spoofed Traffic Mix  
All the surveyed works in Section 2 that use a rolling window on collected RSSI stream(s) for the purposes of signal classification (i.e., authentication) have implicitly assumed that each window of length \(n\) may fully consist of RSSI values from either an attacker or a legitimate device. However, this is not a realistic assumption given the unknown motivation and capabilities of adversaries. Moreover, many modern-day IoT devices (e.g., especially those used in home automation) are not battery operated and/or are not much concerned with energy preservation and as a result may be in frequent communication with other nearby devices. Consequently, in any given window of length \(n\) (used by the classification engine) there may exist some mix of the legitimate node's and the adversary's RSSI values as depicted in Figure 10.
## 4 Results
### Impact of Temporal and Satellite Retrieval-Specific Sampling on IWV Aggregations and Distributions
More specifically, the low temporal sampling of the satellite instruments and retrieval-specific limitations only lead to small biases of \(-0.06\) kg/m\({}^{2}\), \(-0.25\) kg/m\({}^{2}\), \(-0.05\) kg/m\({}^{2}\) and \(-0.05\) kg/m\({}^{2}\) for IASI, MIRS, MODIS and MODIS-FUB, respectively. The overall biases, obtained by comparing directly all matched GPS and satellite IWV values, are much larger, indicating that the impact of the performance of the retrieval algorithms clearly dominates in the mean statistics. However, a closer look at the statistics listed in Tables 3 and 4, corresponding to the distributions shown in Figures 7 and 9, respectively, reveal that for various subsets of the IWV datasets the IWV differences can be as large or even larger than the overall biases obtained from the one-year time period. The 10, 50 and 90 percentiles show that while the overall mean IWV difference is positive/negative, the IWV difference at low, median and high IWV values can be negative/positive. This demonstrates that a mean IWV value is not necessarily representative for the entire IWV distribution. The same can be said for seasonal IWV subsets. For example, while the overall mean IWV difference between the GPS IWV obtained for daytime and certain clear-sky cases and all GPS time steps is nearly zero, the mean IWV difference for the spring period is \(-3.06\) kg/m\({}^{2}\), see Table 3. Table 4 shows that the overall mean IWV difference is clearly smaller using the GPS IWV values with satellite sampling, compared to using the actual satellite IWV values (satellite bias), while this is not necessarily true at the low (10 th percentile) and high (90 th percentile) end of the IWV distributions. On a seasonal basis, the mean IWV differences can be much larger than the overall satellite bias. The largest IWV difference of \(-4.23\) kg/m\({}^{2}\) is found for MODIS in the winter time, which will be related to both low sampling  
Figure 9: IWV distributions for GPS IWV values for all GPS time steps (black), for GPS IWV values for all time steps matched with satellite observations (green), for GPS IWV values for all timesteps matched with satellite observations with valid IWV retrieval (blue), and for all matched valid satellite IWV values (red). conditions due to high cloud coverage as well as challenging retrieval conditions due to high solar zenith angles and snow covered surfaces.
## 1 Introduction  
This paper contributes to the Special Issue \"Results of the II National Research project of AIAr: archaeometric study of the frescoes by [PERSON] and workshop at the church of San Panfilo in Tornimparte (AQ, Italy)\", in which the scientific results of the Second National Research Project conducted by members of the Italian Association of Archaeometry (AIAr) are discussed and collected. The cycle of frescoes is undoubtedly the masterpiece of the painter [PERSON]. The wall paintings are framed by an architectural score, which creates illusionistic effects, running along the perimeter of the apse. A beautifully painted vault crowns the whole, and the sequence of images gives a beautiful 'cinemascope' effect.  
The colourful scenes, subject to periodic restorations, are the subject of numerous studies, as highlighted by the vast literature.  
Preliminary investigations of the painting technique, as well as the restoration materials of the pictorial cycle, were carried out using spectroscopic analysis and mainly non-invasive techniques, such as multiband imaging and single-spot techniques already optimized for works of art in cultural heritage [1; 2; 3].  
The multispectral investigations carried out prior to the sampling campaign highlighted a diffuse presence of retouching, conservation and consolidation materials. Moreover, evident detachment and exfoliation phenomena were observed, together with a strong efflorescence. Chemical characterizations of the soluble salts by means of ion chromatography and attenuated total reflectance Fourier-transform infrared spectroscopy have been performed within the AIAr project [4]. Although the Renaissance pictorial cycle is of national interest, the pictorial technique has not yet been defined. Furthermore, the characterization of the consolidation and protective materials is necessary for the restoration, which aims to recover the legibility of the work, undermined by the degradation of the surface materials. For these purposes, within the research project, it was decided that twelve samples would be collected for chromatographic analyses. Currently, several works have demonstrated the capability of characterizing natural organic materials of different natures, such as lipid, protein, resinous and waxy materials with gas chromatography techniques coupled with mass spectrometry [5; 6; 7; 8; 9; 10] and pyrolysis [11]. The pyrolysis analysis is also essential for the investigation of synthetic products used in restoration through the study of markers produced by the pyrolysis [12; 13; 14].  
Despite the heavy and numerous restoration interventions, some areas were found to be of particular interest for the investigation of the paintings, such as residues of original gilding decorations, or original areas without overpainting or where aged coating films were less abundant.
# SR-Inpaint: A General Deep Learning Framework for High Resolution Image Inpainting
## 3 Method
### SR Network  
Since only LR inpainted results can be obtained from LR inpainting network, in addition to the unavoidable blur and noise in the inpainting process, it is necessary to address the problem of high-frequency information reconstruction at high resolution. Hence, the SR network aims to recover the missing high-frequency information in the HR field-of-view and enhance the resolution of LR inpainted results. Since the blurring pattern of the generated LR content is unknown, we adopt a deep plug-and-play SR framework for arbitrary blur kernels (DPSR) [40].  
Most existing SR methods assume some degradation model. A widely used general degradation model for SR is depicted as Equation (5).  
\[\mathbf{y}=(\mathbf{x}\otimes\mathbf{k})\downarrow_{s}+\mathbf{n} \tag{5}\]  
where \(\mathbf{x}\otimes\mathbf{k}\) means the convolution between blur kernel \(k\) and HR image \(x\). \(\downarrow_{s}\) is a subsequent downsampling operation with scale factor \(s\), and \(n\) is additive white Gaussian noise (AWGN).  
However, DPSR employs a new degradation model that supports blur kernel estimation using existing deblurring methods. As shown in Equation (6), the degradation model of DPSR made a modification to the general degradation model by first bicubicdownsample the full size image and then convolution with kernel \(K\), rather than the convolution-downsample order, which is effective in dealing with blurry LR image.  
\[\mathbf{y}=(\mathbf{x}\downarrow_{s})\otimes\mathbf{k}+\mathbf{n} \tag{6}\]  
Both models are then plus a noise term \(n\). Once the model is defined, an energy function is formulated according to Maximum A Posteriori (MAP) probability, which contains two terms: data fidelity (likelihood) term and a regularization term. This optimization problem is solved with a quadratic splitting (HQS) algorithm.  
Later, a super-resolver needs to be specified, which should also take the noise level as input. Here, we only need to modify the existing DNN-based super-resolver by adding a noise map as input. Methods such as SRMD can also be adopted as they already contain the noise level map.
## 3 Optimization Results
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Parameter**} & \multicolumn{2}{c}{**Before Optimization**} & \multicolumn{2}{c}{**After Optimization**} \\  & \multicolumn{2}{c}{**(Figure 3a)**} & \multicolumn{2}{c}{**(Figure 3b)**} \\ \hline Speed, rpm & 14,400 & 18,000 & 14,400 & 18,000 \\ \hline Current, A (RMS) & 6.42 & 8.68 & 6.06 & 8.48 \\ \hline Efficiency, \% & 84.3 & 85.0 & 84.2 & 84.2 \\ \hline Total losses, W & 70.5 & 130.1 & 70.8 & 138.1 \\ \hline Mechanical power, W & 386 & 754 & 386 & 754 \\ \hline Electric power, W & 447 & 869 & 448 & 877 \\ \hline Copper losses, W & 44.1 & 80.5 & 48 & 94.2 \\ \hline Stator core losses, W & 10.7 & 24.7 & 9.1 & 21.8 \\ \hline Rotor core losses, W & 5.8 & 8.4 & 4.1 & 6.5 \\ \hline Magnet losses, W & 0.8 & 1.4 & 0.4 & 0.8 \\ \hline Duty cycle & 0.181 & 0.374 & 0.205 & 0.425 \\ \hline Minimal torque, N-m & \(-\)0.22 & \(-\)0.1 & 0.081 & 0.089 \\ \hline AMinDT, N-m & 0.476 & 0.5 & 0.175 & 0.311 \\ \hline PPTR, N-m & 1 & 1.05 & 0.381 & 0.668 \\ \hline PPTR, \% of the average value & 391 & 263 & 149 & 167 \\ \hline Objective function, F & \multicolumn{2}{c}{0.61884} & \multicolumn{2}{c}{0.32119} \\ \hline \hline \end{tabular}
\end{table} 
Table 4: The FRM characteristics before and after the optimization. Figure 9: Change of the motor torque oscillations during the optimization.
# A Novel Method for Spectrometry Based on Imaging Systems
## 2 Materials and Methods  
### Experimental Set Up  
Measurements were performed with a round shape (52 mm diameter and 6.25 mm thickness) CRY018 monolithic crystal. It was tested with an MAPMT equipped with an SBA (Super Bialkali) photocathode which guarantees a high quantum efficiency (up to 35%). All surfaces, except the one in contact with the photocathode, were polished and covered with white painting (0.2 mm of TiO\({}_{2}\)). White painting is a typical feature for spectromrtical application increasing light collection [12; 13].  
The multi anode PMT used was a Hamamatsu H10966A-100 [20] with 64 independent anodes, made by a metal channel dynodes structure and a segmented anode pad of 8 \(\times\) 8 array (6.08 mm pitch, 49 \(\times\) 49 mm\({}^{2}\) active area). It reaches gains up to \(3\times 10^{5}\), a first dynode efficiency of 60% and an anodic gain variability of 1:2. Standard PMT collection efficiencies are usually higher, ranging from 80% up to 100%. This means that the best choice to have a system with imaging capabilities while maintaining similar or better performances in terms of efficiency and energy resolution requires the use of SiPM instead of MAPMT. A picture and a schematic top view of the MAPMT are shown in Figure 1.  
Four analog boards acquire in parallel the 64 anodic signal from the MAPMT, each channel being equipped with an operational amplifier in charge integration configuration (with an integrating capacitance of 22 pF and an input-output and supply range of \(\pm\)10 V), a low injected charge (less than 1 pC) switch. After that a scintillation event has occurred in the crystal, constant voltages, proportional to the injected charge are present at the operational amplifier outputs. Two ADCs (analog to digital converter) with 14 bit resolution and eight parallel channels make the digital conversion. The hold signal is simultaneous on all of the 16 channels while the readouts are made sequentially and can be read on a parallel 14 bit bus common between two ADCs. The ADC sampling rate is programmable up to 250 Ksample/s, that is the maximum readout frequency of the system. The ADC  
Figure 1: Multi anode PMT Hamamatsu with its schematic top view [20]. (**a**) Multi anode PMT Hamamatsu H10966A-100. (**b**) Anode matrix view.  
control and readout is performed by an FPGA board (field programmable gate array), that analyzes the data read in each time slot and discards those below a certain threshold. The FPGA firmware controls all the signals of the 4 analog PCBs and the download of the ADC collected data creating a suitable packet for the subsequent USB transmission. A picture and a schematic block of the whole system is shown in Figure 2.
# Image Copy-Move Forgery Detection Based on Fused Features and Density Clustering
## 5 Experimental Results
### Copy-Move Forgery Detection
#### 5.4.2 Post-Processing Forgery Detection  
This paper focuses primarily on post-processing operations for detecting tampering in images using two methods: noise and Gaussian blur. The detection results were compared and analyzed by comparing the visual detection effect with the binary image given in the dataset.  
Gaussian blur forgery detection: To evaluate the detection performance of the proposed method on Gaussian blur forgery, we selected two blur factors, \(\sigma=0.5\) and \(\sigma=2\). The detection results are shown in Figure 9. The first row in the figure shows the original images, the second row shows the binarized truth images provided in the dataset, and the third row shows the detection results of the proposed method. When the Gaussian blur factor is set to \(\sigma=2\), it can be observed from the comparison between the detection results of the proposed method  
Gaussian white noise forgery detection: To verify the detection performance of the proposed method on Gaussian white noise tampering, we also tested the images in the dataset by adding Gaussian white noise with mean \(m=0\) and variance \(v=0.001\) and \(v=0.0005\). The detection results with Gaussian white noise forgery detection are shown in Figure 10. The first row shows the original images, the second row shows the binary images provided in the dataset, and the third row shows the experimental test results obtained in this study. It can be observed from the comparison between the experimental detection results (Figure 10, third row) and the binary images (Figure 10, second row) that the proposed method had no missed detection and accurately located the tampered regions with Gaussian white noise, demonstrating its effectiveness in detecting Gaussian white noise forgery.  
The comparison between binarized truth images and detection results in Figures 9 and 10 shows that there are almost no missed detection regions with the proposed method. Whether in noise forgery or Gaussian blur forgery detection, the detection results of this paper are almost identical to the binary images provided in the dataset. This indicates that the proposed method demonstrates excellent performance in post-processing operations, such as Gaussian blur forgery and Gaussian white noise. Overall, the proposed method can accurately locate tampered regions, especially when the selected blur factor and noise variance are small, and the detection results are more accurate.  
The results of the \(F_{1}\) score for the proposed method and the two comparison algorithms are shown in Figure 11. From the figure, it can be observed that the proposed method has a significant advantage in detecting Gaussian noise and blur forgery.  
Figure 10: Effect images of Gaussian white noise forgery detection. **First row**: original images; **second row**: binarized truth images; **third row**: effect images of forgery detection.
# An Analysis of MPTCP Congestion Control
## 4 Multi-Homing Solutions
### Link Layer  
At the link layer, link aggregation mechanisms have been introduced to aggregate the capacity of switches' multiple interfaces [2]. Generally, the interfaces are aggregated under one logical interface and share the same MAC address. This results in the upper layers being unaware of the link aggregation. The virtual interface aims to increase throughput, provide redundancy and distribute traffic load between switches [2].  
The Link Aggregation Control Protocol (LACP) [12] is part of the IEEE 802.3 ad specification which allows bundling of multiple physical ports into a single virtual interface. The protocol provides automatic configuration and link aggregation as well as the ability to dynamically detect link failure [12].  
LACP exchanges LACP packets between LAN ports in active or passive modes. Active mode starts the negotiation while passive mode responds to the packet negotiation. The protocol identifies the capabilities of each port group dynamically and exchanges this information between the LAN ports. The protocol requires synchronisation between the switches. Further, the switch can only aggregate its links to the next-hop. If the bottleneck is not the next hop, the aggregated links do not provide any benefit to the network bottleneck [12, 13].
## 4 Mathematical Model and Optimization Framework CFD Numerical Simulation Setup
### Optimization Workflow  
As illustrated in Figure 4, the proposed diesel conditioning optimization framework proceeds as follows:  
1. Initial Population Generation: A random population satisfying path and parameter constraints is generated using MATLAB R2023a-GA. The conditioning parameters are embedded into Fluent journal files.
2. CFD Simulation: The journal files are executed in a pre-configured Fluent case to simulate flow and mixing [24].
3. Fitness Evaluation: The simulation results are parsed, and the performance of each individual is evaluated based on conditioning effectiveness.
4. Convergence Check: If the convergence criterion is met, the optimal result is recorded. If not, the population is updated via selection, crossover, mutation, generation gap, and elitism.
5. Population Update: New individuals are translated into journal files, and the simulation is repeated.
6. Iterative Optimization: Steps 2-5 are repeated until the termination condition is satisfied and the global optimum is found.  
Figure 4: Schematic of the optimization design process integrating MATLAB and ANSYS Fluent 2022R1. The workflow includes population generation, circulation check, journal file modification, CFD simulation, fitness evaluation, and genetic operations until convergence.