esa-sceva/satcom-chunk-collection huggingface dataset:
# Windowed Joint Detection and Decoding with IR-HARQ for Asynchronous SCMA Systems
## 4 Simulation Results  
To demonstrate the effectiveness of the proposed algorithm, we compare the performance of the original joint detection and decoding algorithm (According to Section 1.2, limited studies have been conducted on the decoding algorithm for the SCMA system with HARQ in recent years. Additionally, the innovative approaches presented in [18; 19] are designed for the SCMA system with CC-HARQ, not the SCMA system with IR-HARQ), which can be obtained by setting the window size to \(w=1\), with the performance of the proposed algorithm.  
Considering the SCMA system with an RC-LDPC encoder, the information length is \(k=256\), and the block length transmitted each time is \(I=400\). Then, the initial block length and code rate are \(N_{0}=400\) and \(R_{0}=0.64\). We assume that there are \(J=6\) users sharing \(L=4\) resource elements. The maximum number of transmissions for one information block is \(D_{\text{max}}=4\), and the window size changes from \(w=1\) to \(w=4\).  
Figure 6 shows the BER performance comparison of the SCMA system with two detection and decoding algorithms: traditional joint detection and decoding and windowed joint detection and decoding. The system uses a four-point, high-dimensional constellation over the additive white Gaussian noise (AWGN) channel, with an outer iteration of \(\text{Iter}_{\text{out}}=3\) and an inner iteration of \(\text{Iter}_{\text{in}}=5\). It is shown in Figure 6 that with the increase in \(w\), the BER performance of windowed joint detection and decoding algorithm improves. In addition, when \(w\) is large enough (\(w=3\) here), the BER performance improves slightly with the increase in \(w\). We also see that the SCMA system using the windowed joint detection and decoding algorithm with \(w=3\) outperforms the traditional joint detection and decoding algorithm, achieving about a 0.5 dB improvement in BER performance.  
Figure 7 compares the throughput of the traditional joint detection and decoding algorithm and the windowed joint detection and decoding algorithm. The throughput is denoted as the actual rate of RC-LDPC codes used to decode the fixed information blocks correctly. At low SNRs, both performances approach zero, while at high enough SNRs, both are good. The comparison shows that the SCMA system with the proposed algorithm has better throughput.  
Figure 6: BER performance of the SCMA system with the IR-HARQ scheme using the windowed joint detection and decoding algorithm with \(D_{\text{max}}=4\).
## 3 Results
### High-Energy Spectroscopy Experiment Results
\begin{table}
\begin{tabular}{c c c c c c} \hline \multicolumn{6}{c}{The thermal neutron-attenuation parameters of serpentinite samples (25.4 meV)} \\ \hline Parameter & Sr-1 & Sr-2 & Sr-3 & Sr-4 & Sr-5 \\ \hline \(\Sigma\) (cm\({}^{-1}\)) & 0.6007 & 0.8152 & 0.7132 & 0.6513 & 0.7508 \\ \hline MFP (cm) & 1.6647 & 1.2266 & 1.4021 & 1.5354 & 1.3318 \\ \hline HVL (cm) & 1.1536 & 0.8501 & 0.9717 & 1.0640 & 0.9230 \\ \hline RPE (\%) & 61.7548 & 72.8662 & 68.0544 & 64.7275 & 69.9211 \\ \hline \multicolumn{6}{c}{The fast neutron-attenuation parameters of serpentinite samples (4 MeV)} \\ \hline Parameter & Sr-1 & Sr-2 & Sr-3 & Sr-4 & Sr-5 \\ \hline \(\Sigma\) (cm\({}^{-1}\)) & 0.3405 & 0.3820 & 0.3564 & 0.3565 & 0.3599 \\ \hline MFP (cm) & 2.9367 & 2.6181 & 2.8061 & 2.8051 & 2.7788 \\ \hline HVL (cm) & 2.0351 & 1.8143 & 1.9447 & 1.9440 & 1.9257 \\ \hline RPE (\%) & 42.0061 & 45.7264 & 43.4575 & 43.4692 & 43.7744 \\ \hline \multicolumn{6}{c}{The fast neutron removal-attenuation parameters of serpentinite samples} \\ \hline Parameter & Sr-1 & Sr-2 & Sr-3 & Sr-4 & Sr-5 \\ \hline \(\Sigma\) (cm\({}^{-1}\)) & 0.0819 & 0.0876 & 0.0842 & 0.0835 & 0.0864 \\ \hline MFP (cm) & 12.2091 & 11.4222 & 11.8831 & 11.9761 & 11.5690 \\ \hline HVL (cm) & 8.4611 & 7.9154 & 8.2347 & 8.2996 & 8.0172 \\ \hline RPE (\%) & 12.2824 & 13.0713 & 12.5978 & 12.5058 & 12.9165 \\ \hline \multicolumn{6}{c}{The gamma ray-attenuation parameters of the serpentinite samples (302 keV)} \\ \hline Parameter & Sr-1 & Sr-2 & Sr-3 & Sr-4 & Sr-5 \\ \hline \(\Sigma\) (cm\({}^{-1}\)) & 0.2585 & 0.2773 & 0.2689 & 0.2632 & 0.2726 \\ \hline MFP (cm) & 3.8682 & 3.6066 & 3.7189 & 3.7988 & 3.6682 \\ \hline HVL (cm) & 2.6806 & 2.4994 & 2.5772 & 2.6326 & 2.5421 \\ \hline RPE (\%) & 33.8756 & 35.8295 & 34.9641 & 34.3731 & 35.3499 \\ \hline \end{tabular}
\end{table} 
Table 5: The radiation-shielding parameters of serpentinite samples.
# A Privacy-Preserving Protocol for Utility-Based Routing in DTNs
## 4 System Design of PPUR
### Security Analysis  
We consider two types of attacks, namely passive and active attacks.
# SDN-Based Routing Framework for Elephant and Mice Flows Using Unsupervised Machine Learning
## 2 Literature Review
Overall, it is found that none of the existing approaches for flow routing in SDNs have proposed an application for identifying mice and elephant flows, enabling flow type and topology-aware routing. The objective of this paper is to propose a framework that can accomplish this by using two blocks, namely, external application and traffic analysis. Furthermore, this work proposes a mechanism for routing by using the short routes for mice flows and the widest routes for elephant flows. The cost of routing is computed recursively, which provides more efficiency and enables consistency with real-time constraints.
# Low-Cost Assessment Method for Existing Adjacent Beam Bridges
## 4 Lateral Load Distribution Performance Rating Number LDN
\(S_{n}\) is the influencing extent coefficient of joint-\(n\) on the LLDF of beam-\(i\); its value depends on \(N_{\gamma}\) and the number of beams spaced between joint-\(n\) and beam-\(i\); the value of \(S_{n}\) is presented in Table 3; \(S_{n,ref}\) is the corresponding maximum value. \(P_{n}\) is the position coefficient of joint-\(n\); if the damaged joints occur in pairs on both sides, \(P_{n}=0.6\) for joints located on the side with more beams, \(P_{n}=0.4\) for joints located on the side with fewer beams, and \(P_{n}=0.5\) where the numbers of beams on both sides are the same; when the beam being analyzed is the exterior beam or there is no corresponding joint on the other side, \(P_{n}=1\). For example, when studying beam-3 of an ARHB bridge with nine beams, if the numbers of damaged joints are joint-1 to 3 and 5 to 7, the position coefficients are shown in Table 4.  
The value of \(D_{n}\), \(N_{\gamma}\), \(S_{n}\), and \(P_{n}\) is determined by the expert evaluation method. It should be noted that the method proposed in this article only evaluates the influence of transverse connections, namely, grouted joints and transverse prestressed reinforcement, on the lateral load transmission performance of bridge superstructure. This does not consider the lateral load redistribution caused by the performance degradation of the adjacent rectangular hollow beam. In addition, this method does not involve the performance assessment of the substructure and deck system of the bridge.
## 2 IEEE 802.15.4
The request indicates the GTS Characteristics, which are composed by the GTS Length (number of slots being requested); the GTS Direction: '0' for uplink (sending data) or '1' for downlink (receiving data); and the Characteristic Type: '1' for allocation or '0' for deallocation. On receipt of a GTS allocation request, the PAN coordinator sends back an acknowledgement frame. If there are resources available in the superframe, the PAN coordinator announces the allocation to the device through the inclusion of a GTS descriptor in the beacon. The GTS descriptor has a length of 3 bytes and is composed by the Device Short Address (16 bits), the GTS Start Slot (4 bits) and the GTS Length (4 bits). The GTS descriptor remains in the beacon for _aGTSDescPersistenceTime_ (= 4) superframes, after which it is removed. If a device misses the beacon in the beginning of a superframe, it must not use its allocated GTS slots until it receives a subsequent beacon correctly. The deallocation of a GTS may result in the superframe becoming fragmented. In this case, the PAN coordinator must perform a GTS reallocation to remove the gap in the CFP in order to maximize the CAP length.
## 3 Results and Discussions
Below 200 Oe, both NRs and NTs presented a negligible heating efficiency but above 200 Oe, the SAR values tended to increase; more rapidly for the NRs than for the NTs, reaching a maximum value of \(\sim\)110 and 80 W/g, respectively, at 400 Oe. The obtained SAR values were smaller than those reported for other similar hollow nanostructures, e.g., [PERSON] et al. [41] reported SAR values up to 426 W/g for magnetite NRs measured at 200 Oe and 300 kHz. Nevertheless, as depicted, the SAR vs. field curves were still far from saturation and, therefore, the SAR values could keep rising by increasing the applied field but this could pose a risk for the safety of the patient [18]. The current clinical safety limits indicate that the product of the field amplitude \(\times\) frequency \(\leq 5\times 10^{9}\) Am\({}^{-1}\)s\({}^{-1}\)[63]. This implies that for a frequency of 300 kHz, as in our case, the maximum applied field should not be higher than 208 Oe. In this range, the NRs still provided a better heating efficiency than the NTs although in both cases the SAR values were very small. Therefore, these results indicated that at low fields, H \(<<\) H\({}_{\rm K}\), the MNPs with a lower effective anisotropy performed better as heating agents than those with a higher effective anisotropy [25]. The effect of the intraparticle interactions in the heating efficiency of NRs and NTs could be even better visualized if we compared the AC hysteresis loops and the SAR vs. H curves of the NRs and NTs with those measured for MNPs very similar to their corresponding nanograins: 15 nm nanospheres (see [56]) and 65 \(\times\) 6 nm nanorods (see [35]), respectively. As depicted in Figure S4 (see Supplementary Information), the AC hysteresis loops for both nanorods and nanospheres were more squared and saturated than those obtained for the NRs and NTs. The differences were especially remarkable in the case of the nanorods. This could also be observed in the SAR vs. H curves: the SAR values measured for the nanorods and nanospheres were higher than those obtained for the NTs and NRs for all of the fields analyzed. The nanorods and nanospheres reached a maximum SAR value of 300 W/g and 140 W/g, respectively, at 400 Oe and 300 kHz. These values were 2.7 and 1.7 times higher than those obtained for the NTs and NRs, respectively. The higher  
Figure 4: AC magnetometry loops obtained for (**a**) the iron oxide NRs and (**b**) NTs at 300 kHz with H\({}_{\rm max}\) = 400 Oe. (**c**) SAR vs. H curves derived from the AC hysteresis loop area (H = 0–400 Oe, f = 300 kHz). difference between the SAR values of the NTs and nanorods indicated the higher impact of the intraparticle interactions in the NTs compared with the NRs.
## 2 Data and Methods
### The Relative Radiometric Correction  
A relative sensor normalization method was presented based on the single-scene statistics, combined with the traditional histogram matching method. The method includes four principal steps: image cropping, histogram matching, mean adjustment, and image stitching. The B1 band image along with the panchromatic image were used as an example to present the data processing workflow that is illustrated as Figure 4.  
Figure 3: (**a**) The curves of DN values of the rows of the panchromatic band (B0) and three multispectral bands (B1–B3) for the mosaic image for the Antarctic region shown in Figure 2. The red box highlights the DN values are the row mean DN values from the center columns of the CMOS1 and CMOS2 images; (**b**) Single band mosaic image of the mosaic image for the Antarctic region shown in Figure 2. The mosaic image is composed of band B1 of two CMOS images (**Left**: CMOS1 and **Right**: CMOS2).  
(1) Image cropping  
The B1 image and the panchromatic image both have identical dimensions, which are 5000 rows by 5056 columns. Both images are divided into 8 strips evenly along the column direction. As shown in Figure 5, from left to right the strip i is denoted as [Xi] for the B1 image and {Yi} for the panchromatic image. Each strip is expanded with 50 pixels towards both sides, except the strips {X1} and {X8} as well as {Y1} and {Y8}, which only expanded 50 pixels towards the inner side. Once the image cropping is completed, the parameters such as center FOV and the edge FOV of the image are specified. X1 is the center FOV when the camera is operated in a forward scanning mode, while X8 is the center FOV when the camera is in a reverse scanning mode. It is notable that the imaging cameras CMOS1 and CMOS2 move forward and backward, respectively, in the northern hemisphere. The scanning modes are switched in the southern hemisphere.  
Figure 4: Flow diagram for the improved histogram matching algorithm.  
Figure 5: Schematic diagram of image cropping.
# Automatic Restoration of Dunhuang Murals and Process Visualization Method Based on Deep Learning
## 3 Method  
### Dataset Construction  
Owing to the wide recognition of the Dunhuang murals, many photography and scanning sources can be used. In particular, the construction of digital Dunhuang mural images enables the preservation of numerous high-quality murals, creating good conditions for the application of computer technology. A total of 2000 high-definition color photographs of Dunhuang murals were collected in this study (including from Dunhuang Caves 009, 014, 045, and 085). To ensure that the entire dataset contains rich features of Dunhuang mural paintings, the mural subjects involved various elements, such as portraits, costumes, architecture, and plants.  
Data preprocessing: Dunhuang murals have many defects of different sizes. These are natural subjects for inclusion in datasets for image restoration. However, mural images captured manually may suffer from perspective deformation at the edges or other defects. Therefore, the collected mural images must be preprocessed before they can be used for model training. The preprocessing procedure involved random cropping and manual filtering of mural images to obtain the training dataset and validation samples. This approach retained the local details and rich textures of the patterns. Figure 1 shows the data preprocessing.  
Figure 1: Dunhuang murals image preprocessing process.  
Firstly, we cropped all the original images at random locations to obtain a large number of cropped images. The cropping sizes were set to 256 \(\times\) 256, 512 \(\times\) 512, and 768 \(\times\) 768 pixels. Next, we manually filtrated the cropped images. The criteria for manual filtering: (1) retain only parts with clear outlines, no defacement, and a perspective view of the images; (2) delete background images with no patterned content. Finally, we uniformly compressed the filtered images to 256 pixels and used them as a training set for the model. The purpose of manual filtering is to select the most representative images to ensure the utility and effectiveness of the dataset for subsequent restoration research. We conduct multiple rounds of iterative reviews to check for and correct any omissions or errors in the manual filtering process.  
We panned, flipped, and rotated the images in the training set to create a Dunhuang mural dataset of 26,240 images. We added the original mural images to the training set to increase the accuracy of the model testing and manually cropped them for model testing.
# Collusion Attack Resilient 3D Mesh Watermarking Based on Anti-Collusion Fingerprint Code
## 5 Discussion and Conclusions  
The main contribution of our work is a collusion resilient watermark based on the anti-collusion fingerprint code. In contrast to the existing robust mesh watermarking, our proposed method has sufficient capacity to carry the anti-collusion fingerprint code. To minimize the detection error, we also modeled the response of the detector and presented optimized thresholds for our method. Based on experiments with a public benchmark, the proposed method outperformed the conventional robust mesh watermarking in all cases.  
Nowadays, the demand for 3D mesh watermarking has increased due to the emergence of low-cost 3D printers. On the other hand, copyright issues will inevitably occur with the expansion of 3D model sharing platforms such as Thingiverse, Pinshape, and Sketchfab. In spite of various watermarking methods, mesh watermarking techniques resilient to collusion attack were rarely considered. Therefore, we believe that the proposed method can give advantages to our industry as well as the academic society.  
Conceptualization, J.-U.H.; Investigation, J.-U.H. and I.-J.Y.; Methodology, J.-U.H.; Resources, I.-J.Y.; Software, J.-U.H.; Supervision, H.-K.L.; Validation, I.-J.Y.; Writing of the original draft, J.-U.H.  
This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIP) (No. 2016R1A2B2009595) and by the Institute for Information & communications Technology Promotion (IITP) grant funded by the Korean government (MSIP) (No. R0126-15-1024, Managerial Technology Development and Digital Contents Security of 3D Printing based on Micro Licensing Technology).  
The authors declare no conflict of interest.
# Optimizing Lattice Basis Reduction Algorithm on ARM V8 Processors
## 4 Experimental Results and Performance Analysis
### Correctness Verification
#### 4.2.2 Residual Check  
The residual matrix \(\mathbf{R}\) is defined as the difference between the result of the transformation of the original lattice matrix \(\mathbf{B}\) by the integer matrix \(\mathbf{U}\) and the prescribed basis matrix \(\mathbf{B}^{\prime}\):  
\[\mathbf{R}=\mathbf{B}\mathbf{U}-\mathbf{B}^{\prime} \tag{9}\]  
We use the Frobenius norm to measure the size of the residual matrix \(\mathbf{R}\).  
\[\left\|\mathbf{R}\right\|_{\mathbf{F}}=\sqrt{\mathbf{\Sigma}_{i=1}^{m}\mathbf{\Sigma}_{j=1}^{ m}\left|\mathbf{r}_{ij}\right|^{2}} \tag{10}\]  
where m is the number of rows in the matrix, n is the number of columns in the matrix, and \(\mathbf{r}_{ij}\) is the element of row i and column j in the matrix.
# Salinity Inhibition in Thermophilic Anaerobic Digestion of Organic Waste
## 2 Materials and Methods  
### Substrate and Hoculum  
The microbial inoculum and substrate (a combination of food waste, separately collected municipal organic waste, and slaughterhouse waste) were taken from a full-scale operating thermophilic anaerobic digester from KOTO Ltd. (Ljubljana, Slovenia), which treated the substrate in a conventional thermophilic anaerobic CSTR at 55 \({}^{\circ}\)C, with chloride concentrations ranging from 1 and 1.4 g/L, which corresponded to 2.3 g/L NaCl (assuming that almost all chloride was derived from NaCl). The average total solids (TS) concentration of the original substrate was 8.8 \(\pm\) 0.8%, of which 84.6% were volatile solids (VS). The average total chemical oxygen demand (tCOD) was 100.722 \(\pm\) 9.836 g/kg, and the total nitrogen (TN) concentration was 3.010 \(\pm\) 0.420 g/kg. The substrate contained an average of 0.83 \(\pm\) 0.12% protein and 13.8 \(\pm\) 1.8% total fat. For the experiments, the substrate was prepared in one batch for all experimental series (approximately 400 kg) and stored in a cold room at 2 \({}^{\circ}\)C. Before use, it was diluted 1:2 to avoid interference with other possible  
\begin{table}
\begin{tabular}{c l} \hline \hline
**Authors** & **Literature** \\ \hline [PERSON] et al. [25] & Effects of inhibition by sodium ion and ammonia and different inocula on acetate-utilizing methanogenesis: Methanogenic activity and succession of methanogens. \\ [PERSON] et al. [26] & Deep insights into the anaerobic co-digestion of waste activated sludge with concentrated leachate under different salinity stresses \\ [PERSON] et al. [27] & Research progress of high-salinity wastewater treatment technology \\ [PERSON] et al. [28] & Bioaugmentation with marine-derived microbial consortia in mesophilic anaerobic digestion for enhancing methane production under ammonium or salinity stress \\ [PERSON] and [PERSON] [29] & Effect of salt content on biogas production and microbial activity \\ [PERSON] et al. [30] & Microbial community drivers in anaerobic granulation at high salinity \\ [PERSON] and [PERSON] [31] & Anaerobic digestion of high strength bilgewater with granular sludge: confronting salinity and investigating biomass adaptation \\ [PERSON] et al. [32] & Biomethane potential of sludges from a brackish water fish hatchery \\ \hline \hline \end{tabular}
\end{table}
Table 1: List of latest work in the relevant field.  
inhibitors and to facilitate hydraulic handling in small tubing on lab-scale equipment. The average tCOD of the influent was 49.617 \(\pm\) 4.918 g/kg with a corresponding concentration of 4.47 \(\pm\) 0.38% of TS and TN of 1.518 \(\pm\) 0.157 g/kg. The inoculum was collected from the operating full-scale digester and transferred to the experimental reactors immediately before starting. TS concentration of inoculum was 3.6%, of which 61.7% was VS. The salt content was provided by refined standard cooking salt (NaCl).
## 4 Experimental Discussion
### Accuracy Analysis
#### 4.2.2 Results on the Pavia University Scene Dataset
Compared with the sub-optimal result (MDGCN), our framework can improve the classification accuracy by about 2.5%.
## 3 Results and Discussion
### Statistical Features in Different Terrains
#### 3.2.2 Sl Emg  
The SL peak has the most significant difference among the three types of terrains, decreasing from uphill, to level, to downhill. This is because the push-off force generated by the SL needs to overcome the effect of gravity when walking uphill to make the body move forward, while the activity is the opposite when walking downhill. The MAV, WL and other statistical data follow a similar rule  
Figure 5: The ensemble average of the EMG and GRF signals with \(\pm 1\) SD band in three terrains: (**a-c**) are the tibials anterior (TA) and soleus (SL) EMG profiles of uphill, level ground and downhill, normalized by the 50% of maximal isometric voluntary contraction (MIVC), respectively. (**d-f**) are the GRF profiles of uphill, level ground and downhill, normalized to the body weight (BW).  
Figure 6: The statistical features of the EMG and GRF in the different terrains: (**a**) TA EMG features. (**b**) SL EMG features. (**c**) Group 1 of GRF features. (**d**) Group 2 of GRF features. (* \(p\) < 0.05).  
as the TA, indicating that muscle activity is generally higher when walking uphill. In addition, the co-contraction of the SL with the TA to stabilize the gait when going downhill makes the \"SL 25\" increase. The above phenomenon is also observed in [76].