esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
This study developed a method for identifying unknown IoT devices based on CSCL-Siamese networks and a WVE. First, to ensure that the model is trained with a rich set of contrastive training data, the PNSP algorithm was presented, which combines data visualization techniques with a permutation pairing strategy to generate all positive-negative sample pairs while eliminating redundant ones. The decision boundary exhibited bias due to an insufficient number of positive sample pairs. To address this, we improved the classical contrastive loss function and trained the sample data using a CSCL-Siamese network. This process enabled the creation of an embedded vector database for known IoT devices. We used this database to filter out embedding vectors of unknown IoT devices by performing pairwise comparisons. Finally, by integrating cost-sensitive weighting factors with a voting ensemble strategy, a UDI algorithm based on a WVE was proposed, which leverages the clustering capabilities of multiple unsupervised clustering algorithms to complete the identification of unknown IoT devices. By identifying three randomly selected unknown IoT devices, the effectiveness of the proposed method was validated. However, this study also has some limitations: First, the Siamese network can only process a single fingerprint feature due to only grayscale images being utilized to construct the device fingerprints. Combining image data with other fingerprint features to form a new, unified IoT device fingerprint can be considered in future studies. Second, the proposed CSCL-Siamese network requires considerable known IoT devices for training before unknown device identification can proceed. Thus, methods for identifying unknown devices that do not rely on data from known IoT devices will be our next studies.  
Conceptualization, J.Q., W.Z. and X.L.; methodology, W.Z.; software, X.L.; validation, X.L. and W.Z.; formal analysis, J.Q.; investigation, W.Z.; resources, Z.L.; data curation, X.L.; writing--original draft preparation, X.L.; writing--review and editing, W.Z.; visualization, W.Z.; supervision, J.Q.; project administration, X.L.; funding acquisition, Z.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Smart Manufacturing New Model Application Project Ministry of Industry and Information Technology (No.ZH-XZ-18004), the Fundamental Research Funds for the Ministry of Education (No.JUSRP 211A41), the Fundamental Research Funds for the Central Universities (No.JUSRP42003), and the 111 Project (No.B2018).  
Not applicable.  
The raw data supporting the conclusions of this article will be made available by the authors on request.  
The authors declare no conflict of interest.
# ESL: A High-Performance Skiplist with Express Lane
## 6 Evaluation  
### Experimental Environment  
**Hardware.** We performed experiments on a machine equipped with two NUMA sockets, each featuring an Intel Xeon Gold 5318Y processor with 24 physical cores. The machine offers a memory space of 768 GB DDR4 DRAM.  
**Workload.** We used the YCSB workload [44], one of the representative workloads of key-value stores, as well as the Amazon AWS OpenStreetMap workload [45] to represent real-world scenarios for evaluating the performance of ESL.  
In the YCSB evaluation, the YCSB workload consists of four workloads, workloads A-D. Workload A is composed of 50% of the update operations and 50% of the read operations. Workload B and workload D are composed of 5% of update operations and 95% of read operations. Workload C is composed of 100% of the read workload. Note that we did not include the results of the workload E evaluation, as the competitors did not implement the scan operation.  
In the real-world workloads, we used three datasets, denoted as _longitudes_, _longlat_, and _lognormal_. The workloads are composed of the combinations of read and insert operations. We conducted two types of workloads--the read-intensive workload and write-intensive workload--similar to the previous study [34]. The read-intensive workload consists of 95% of read operations and 5% of insert operations. In the write-intensive workload, the ratio of the read operations and the write operations is the same (i.e., 50% read operations and 50% insert operations).  
In each experiment, we inserted 1 million 8-byte keys and 8-byte values in the load phase. After inserting the key-value pairs, we conducted the evaluation with 1 million operations. For the default settings of competitors, the maximum level value was \(\log_{2}\left(number\_of\_data\right)\)[46, 47]. Similar to previous work [26, 27, 46], we used the default  
Figure 7: An illustration of the operation log. A head is updated by the background threads and a tail is updated by the foreground threads.  
maximum level of 19 (i.e., \(\log_{2}(1 Million)\)). We also evaluated the maximum level of 5. We denoted the maximum level of 19 as the high level and the maximum level of 5 as the low level.  
**Competitors** We compared the performance of ESL against other skiplist data structures. We included the No Hot Spot Skiplist (NHSSL) [26] and NUMASK [27]. We used the publicly available version of NHSSL and NUMASK in Synchrobench [46,47]. We could not evaluate the Cache-Sensitive Skiplist (CSSL) [24] because the open-sourced version did not consider the multi-thread and randomized key insertion. The implementation of the Parallel in-memory Skiplist (PSL) [25] was not open-sourced as of writing this paper.
# LightVSR: A Lightweight Video Super-Resolution Model with Multi-Scale Feature Aggregation
## 4 Results
### Ablation Study on HT-Convolution  
To validate the effectiveness and contributions of the HT-Conv module, we conducted an ablation study to compare the performance of the LightVSR model with and without the HT-Conv module. The ablation study was performed on three widely used benchmark datasets: REDS4, Vimeo-90k-T, and Vid4. The results are summarized in Table 2 below.  
From the ablation study, it is seen that the inclusion of the HT-Conv module significantly improves the performance of the VSR model across all evaluated metrics and datasets. Specifically, the application of the HT-Conv module increases the parameter number by 0.14 million. Despite this modest increase in parameters, the model demonstrates a significant improvement in both PSNR and SSIM scores. For example, in the REDS4 dataset, the HT-Conv module achieves a 3.56 dB gain in PSNR and a 0.11 increase in SSIM. These results highlight the effectiveness of the HT-Conv module in enhancing the model's generalization ability and overall performance. The experimental results demonstrate that the HT-Conv module significantly enhances the quality of video super-resolution, particularly in preserving fine details and structural integrity.  
Figure 6: Qualitative comparison of the Vid4 datasets: (**a**) walk, (**b**) foliage, (**c**) city, (**d**) calendar.
# An Internet of Things Access Control Scheme Based on Permissioned Blockchain and Edge Computing
## 4 System Model
### Access Control Process  
The scheme simulates the entire access request method of the user device (subject), inquiring for access to the sources of the IoT device (object), and the particular access control manner is proven in Figure 4.  
Step 1: The subject initiates a session creation communication request to ESA, provides its own identity ID, and sends a registration/authentication request.  
Step 2: ESA returns the result of authentication to the principal.  
Step 3: After the authentication end result is verified, the guest agent device will acquire the statistics data sent through the subject.  
Step 4: After receiving the access request from the subject, the agent device obtains the address of PDC.  
Step 5: The address of the PDC will be sent to the proxy device by the Management Contract (MC) on the PAP.  
Step 6: The proxy device decides access based on key information by calling the PDC on the PAP.  
Step 7: After the PDC receives the access decision task, it obtains the device attribute information in the PIP.  
Step 8: PIP returns attribute information to the PDC.  
Step 9: The PDC makes access control judgments based on the relevant information obtained.  
Step 10: The PC determines the access control and feeds the result back to the PDC as \"true\".  
Step 11: The PDC makes a final judgment on the feedback results and returns them to the proxy device.  
Step 12: After waiting for the access authentication to be finally passed, the proxy device starts accessing the guest resources.  
Step 13: The proxy device performs operations on the guest resource and receives the returned operation results.  
Step 14: The result of the operation is returned to the principal.
# Simplified Derivative-Based Carrierless PPM Using VCO and Monostable Multivibrator
## 3 Proposed Techniques of Demodulation and Modulation
### Proposed Carrierless PPM Techniques  
The following sections analyze several classical carrierless PPM demodulation methods. Despite their different implementations--whether based on integration, PLL, or quasi-FM-PWM conversion--each approach consistently reveals that the demodulated output is fundamentally the derivative of the original message signal. Recognizing this common outcome motivates the development of a new modulation technique, presented  
Figure 4: A block diagram of general PPM demodulation.  
Figure 3: Pulse carrier signal obtained from the third term of Equation (7).  
Figure 2: Subtraction of delayed and non-delayed PWM signals, resulting in the PPM signal.  
in the subsequent section, which explicitly differentiates the message signal prior to PPM generation to simplify the overall system structure.
# Conceptual and Technical Design Aspects of Accelerators for External Injection in LWFA
## 3 Production of Ultra-Short e-Bunches
### Space Charge and CSR Effects
#### 3.3.3 Beam Collimation in a Dispersive Region  
A destructive method for reaching ultra-short electron bunches consists of collimating the longitudinal phase space of the electron bunch by cutting the non-linear tails (as shown in Figure 5). The magnetic compression with a slit between the second and third dipoles of a chicane was initially proposed by Borland to generate 10-20 fs long electron bunches at the Advanced Photo Source (APS) Linac [62]. Recently, it has been demonstrated that this technique can be used for the generation of ultra-short FEL pulses at SLAC and FERMI [63; 64; 60; 65]. The adaption of this system to produce sub-fs bunches is not trivial. Because of the shortness of the electron bunch in the chicane a 3D-CSR model in simulations is needed. At the same time the charge density, being extremely high, requires a 3D space charge model. A detailed study of this problem and a numerical comparison of simulations using different particle codes has been carried out for the first time in [66] and applied to the design of the ARES linac, which we will introduce in the next section.  
Figure 5: Working principle of the longitudinal collimation method in magnetic bunch compression.
# A Dual-Polarized Omnidirectional Rectenna Array for RF Energy Harvesting Harvesting
## 3 Power-Combining Scheme for the Rectenna Array
### RF Power Distributing Mechanism  
Figure 9 shows the schematic of a 3-dB hybrid coupler. Using the transmission line theory, the coupler's 5-parameter matrix is derived and given in (1) below.  
\[S_{ij}=\frac{-1}{\sqrt{2}}\begin{pmatrix}0&0&j&1\\ 0&0&1&j\\ j&1&0&0\\ 1&j&0&0\\ \end{pmatrix} \tag{1}\]  
where \(S_{ij}\) is the transmission coefficient between the ports \(P_{i}\) and \(P_{j}\) of the hybrid coupler.  
If its input ports P1 and P2 are connected to the combined H-port and V-port of the antenna array respectively, then the voltages of the output ports P3 and P4 can be calculated from the \(S\)-parameter:  
\[V_{\text{P3}}=V_{HP}\cdot S_{13}+V_{VP}\cdot S_{23}=\frac{-1}{\sqrt{2}}(jV_{ HP}+V_{VP}) \tag{2}\]  
\[V_{\text{P4}}=V_{HP}\cdot S_{14}+V_{VP}\cdot S_{24}=\frac{-1}{\sqrt{2}}(V_{ HP}+jV_{VP}) \tag{3}\]  
where \(V_{HP}\) and \(V_{VP}\) denote the total voltage of horizontally polarized power and vertically polarized power, respectively. Hence, the power outputs at ports P3 and P4 are  
\[P_{\text{P3}}=\frac{\left|V_{3}\right|^{2}}{2Z_{0}}=\frac{V_{HP}^{2}+V_{VP}^{ 2}}{4Z_{0}} \tag{4}\]  
\[P_{\text{P4}}=\frac{\left|V_{4}\right|^{2}}{2Z_{0}}=\frac{V_{HP}^{2}+V_{VP}^{ 2}}{4Z_{0}}=P_{\text{P3}} \tag{5}\]  
where \(Z_{0}\) is the characteristic impedance of the feeding lines at the coupler's ports. According to (4) and (5), it can be found that the RF power output at port P3 and port P4 are equal. Thus, using two rectifiers in parallel to rectify the two portions of equal RF power, a stable PCE can be achieved.  
Figure 9: Schematic diagram of a 3-dB hybrid coupler.
# Performance Analysis of Ocean Surface Topography
## 5 Conclusions  
As an experimental payload, even though some of the system parameters are limited by the current technique, InIRA has shown its great potential in measuring the OST with high precision that have never been resolved before. Based on the performance analysis of InIRA in this paper, here are some suggestions on parameter design for the future near-nadir InSAR.  
1. According to the intensity boundary phenomenon, the available swath of high altimetric precision will be narrower than expected. As for InIRA, when the incidence range is 1\({}^{\circ}\)-5\({}^{\circ}\), the swath will decrease to only 20 km. This narrow swath can hardly fulfill the altimetry on sub-mesoscale oceanic processes. Therefore, future near-nadir InSAR could fly on a higher orbit to increase the swath width.
2. Flying on a higher orbit means the SNR of the received signal will decrease significantly, thereby definitely increasing the phase noise level. As mentioned before, multi-looking is one of the most effective ways to lower the phase noise, and the 3-cm altimetric precision on a 10-km grid is much higher than that of a 1-km grid, according to the simulation. However, it is quite challenging for the 10-km grid to resolve mesoscale or sub-mesoscale oceanic processes whose physical dimension is only about 15-300 km. Therefore, future near-nadir InSAR should guarantee an even higher intrinsic image resolution so that a larger multi-look number could be acquired on the same grid.
3. Baseline length is the key parameter which guarantees high altimetric precision for near-nadir InSAR. Meanwhile, a longer baseline will mitigate the stringent requirement for a systematic phase noise level and data processing precision, allowing the near-nadir InSAR to achieve better performance much more easily.  
The authors would like to thank the Key Laboratory of Space Utilization, Technology and Engineering Center for Space Utilization, Chinese Academy of Sciences for providing the InIRA data. The authors would also like to thank the anonymous reviewers and the editors for their comments and suggestions that improved the manuscript.  
[PERSON] conceived and performed the experiments; [PERSON] and [PERSON] designed the research and contributed to the article's organization; [PERSON] provided the data for comparative analysis. [PERSON] drafted the manuscript, which was revised by all authors. All authors read and approved the final manuscript.  
The authors declare no conflict of interest.
# Removal of Methylene Blue by Metal Oxides Supported by Oily Sludge Pyrolysis Residues
## 1 Introduction
These studies show that loading metal oxides can indeed improve the ability of composite materials to remove methylene blue. To the best of our knowledge, there has been no study on the use of oily sludge pyrolysis residues supported by metal oxides to remove methylene blue. Therefore, the use of oily sludge pyrolysis residues loaded with metal oxides to prepare composite materials maybe a feasible waste utilization method to remove methylene blue. In this study, residue-supported metal oxide composite materials were prepared by the impregnation method, and oily sludge pyrolysis residue was used as a carrier. With the removal effect of methylene blue as a criterion, the effects of impregnation time, impregnation ratio, the dosage of composite material, contact time, and regeneration performance on the removal of methylene blue were investigated. Then, the loading and removal processes of metal oxides were studied through a series of characterizations and kinetics in order to provide a reference for the usage of oily sludge pyrolysis residues to support metal oxides as adsorbents.
# HARE: Supporting Efficient Uplink Multi-Hop Communications in Self-Organizing LPWANs
## 5 Protocol Stack
### Network Layer
#### 5.3.1 Addressing System  
The addressing system is managed by the GW, which allocates a unique network address to each node during the association process. Nodes will maintain the same network address as long as they do not leave the network. A dynamic record matching the MAC and the network address of all STAs is stored in the GW. The size of the network address is configurable and its value determines the addressing range.
# Temporal Convolutional Neural Network for the Classification of Satellite Image Time Series
## 4 Experimental Results
### How to Control Overfitting?  
Our TempCNN model includes four mechanisms to deal with overfitting: (1) regularization of the weights, (2) dropout, (3) using a validation set and (4) batch normalization. Regarding the optimal architecture in Section 4.4, the model needs to learn a number of parameters higher than more than three times the given number of training instances--2.5M of parameters versus 620,000 training data instances. This section aims at determining which of the used regularization mechanisms are the most important to train the TempCNN network. For this purpose, we first train our TempCNN architectures with only one regularization technique, then with all regularization techniques except one.  
Table 6 displays OA values with or without the use of different regularization mechanisms. The first row displays the results when no regularization mechanism is applied (lower bound), whereas the last row displays the results when all the regularization mechanisms are used (higher bound).  
\begin{table}
\begin{tabular}{r c} \hline \hline  & **Overall Accuracy** \\ \hline
**Nothing** & 90.83 \(\pm\) 0.82 \\ \hline
**Only dropout** & 93.12 \(\pm\) 0.64 \\
**Only batch normalization** & 92.22 \(\pm\) 0.86 \\
**Only validation set** & 91.17 \(\pm\) 0.94 \\
**Only weight decay** & 90.74 \(\pm\) 1.00 \\ \hline
**All except dropout** & 92.07 \(\pm\) 1.20 \\ All except batch normalization** & 92.89 \(\pm\) 0.72 \\
**All except validation set** & 93.68 \(\pm\) 0.60 \\
**All except weight decay** & 93.52 \(\pm\) 0.77 \\ \hline All & 93.42 \(\pm\) 0.76 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Averaged overall accuracy over five runs using different regularization techniques. The first and last rows display Overall Accuracy when all the regularization techniques are turned off and on, respectively. The used dataset is composed of three spectral bands with a regular temporal sampling at two days.  
Table 6 shows that the use of dropout is the most important regularization mechanism for TempCNNs as its only use leads to an OA value close from the one obtained when using the four regularization mechanisms. Conversely, the use of a validation set and the weight decay seem less useful to regularise the model.
## 3 Results and Discussion
### Application as Anode Materials for Li, Na, K, Mg and Ca-Ions Batteries  
We focus now on the application of P-TpG as anode material for batteries. We first estimate the adsorption-energy profiles in the most stable sites, i.e., S1, S2, S3, and S4, depicted in Figure 3. These bonding sites satisfy the highest adsorption-energy with minimum total energy conditions. S1 and S4 are placed inside the nanosheet plane hollow sections, while S2 is on top of the hexagonal part, while S4 can be found out-of-plane. The corresponding adsorption energies of Li-, Na-, K-, Mg-, and Ca-ions over P-TpG are calculated by:  
\[E_{Ad}=E_{S}-E_{P}-E_{At} \tag{1}\]  
where (\(E_{Ad}\)) denotes the adsorption energy, \(E_{P}\) is the total energy of the pristine P-TpG nanosheet, \(E_{S}\) the total energy of the system after metal atom adsorption and \(E_{At}\) refers to the per atom lattice energy of the metal adatoms (At = Li, Na, K, Mg, Ca). The S1 site exhibits the maximum adsorption energies for Li, Na, K, and Ca adatoms with \(-\)2.026 eV, \(-\)2.476 eV, \(-\)3.013 eV, and \(-\)2.428 eV, respectively. Detailed results are presented in Table 1 for all predicted/most stable adsorption sites. Note that the optimal adsorption energy for anode materials should not be close to zero or take positive values. Except for the Mg adatom, all others yield negative adsorption energy values indicating adequate bonding between the considered adatoms and P-TpG substrates. The results indicate that the considered configurations for the Mg atom storage are not suitable since the adsorption energies are very close to zero or positive. For all adatoms, the highest adsorption energies were found in the S1 site. According to Table 1, Bader charge exchange analysis revealed the transferred  
Figure 2: P-TpG electronic band structure and density of states (DOS) are estimated employing PBE functional. The Fermi energy level is shifted to zero. charge values (\(\Delta\)Q) from adsorbed atoms to the substrate for all configurations. The results illustrate that Li and Na adatoms were perfectly ionized over the P-TpG substrates.