esa-sceva/satcom-chunk-collection huggingface dataset:
# Public Key-Based Authentication and En-Route Filtering Scheme in Wireless Sensor Networks
## 4 The Proposed Scheme
### Report Generation
When a current participating node does not provide its new partial signature or provides an incorrect partial signature, \(v_{IDi}\) performs the substitution scheme again. Otherwise, go to Step 6. Step 6. \(v_{IDi}\) sums the \(t\) correct partial signatures to produce the cluster signature: \(\sigma=\sum\limits_{j=1}^{t}\sigma_{j}\) mod \(q(\sigma=(\sum\limits_{j=1}^{t}x_{j}l_{j}d+\sum\limits_{j=1}^{t}k_{j})\) mod \(q=(S_{i}d+k)\) mod \(q\)). Finally, \(v_{IDi}\) sends the signed report \(\{V,e,\sigma\}\) to the BS through multi-hop routing. When a cluster node \(v_{k}\) finds that the signed report sent by the CH is false (according to the steps S1-S4 in Section 4.4), or after the timer \(TRG\) ends, \(v_{k}\) finds \(Fail\_flag=false\) but does not hear the signed report sent by the CH, then \(v_{k}\) performs Algorithm 3 to substitute the CH and to select a new CH to regenerate a signed report. ```
/*Upon a cluster node \(v_{x}\) receiving a SUBMSG message from the CH node \(v_{IDi}\)*/  flg = false; // flg records whether the accusation in the SUBMSG message is true. if (rcd = 0)  if (the \(z\) nodes to be substituted did not send their \(k_{j}P\) to \(v_{IDi}\))  flg = true; else if (rcd = 1)  if (the \(z\) nodes to be substituted did not provide their \(\sigma_{j}\) or provided incorrect \(\sigma_{j}\)) flg = true; if (flg = false)  Perform Algorithm 3 to substitute \(v_{IDi}\); else { for (each node \(v_{IDm}\) to be substituted) { \(TV_{IDm}=TV_{IDm}-0.2\);  if (\(TV_{IDm}<T_{Tv}\) && \(NA_{IDm}<Nc/2\)) {  Broadcast an ALARM message for \(v_{IDm}\); \(v_{x}\), and each cluster node that hears the ALARM message, executes Algorithm 2; } } //end for if (the number of trusted non-participating cluster nodes < \(z\)) {  Fail_flag = true; // Report generation fails. Cancel the timer \(TRG\); } else {  Select the \(z\) trusted non-participating cluster nodes, whose IDs are closer to \(hv\) than the others, to participate in report generation; if (\(v_{x}\) is a new participating node) {  Pick \(k_{x}\) in [1, \(q-1\)] and send \(k_{x}P\) to \(v_{IDi}\);  Compute \(\sigma_{x}\) after receiving \(X(R)\);  Send \(\sigma_{x}\) to \(v_{IDi}\); } else if (\(v_{x}\) is a participating node that has not been substituted) {  Recompute \(\sigma_{x}\) after receiving \(X(R)\);  Send \(\sigma_{x}\) to \(v_{IDi}\); } }
```  
**Algorithm 1**Processing of SUBMSG Message  
```
/*Upon a cluster node \(v_{x}\) receiving an ALARM message for \(v_{y}\) broadcast by node \(v_{z}\)*/ if (it is the first ALARM message sent by \(v_{z}\) for \(v_{y}\)) \(NA_{y}=NA_{y}+1\); if (\(NA_{y}\geq Nc/2\)) {  Delete \(v_{y}\) from the trusted neighbor list;  Add \(v_{y}\) into the isolation list; }
```  
**Algorithm 2**Processing of ALARM Message
# Product Demand Prediction with Spatial Graph Neural Networks
## 2 Related Works
### E-Commerce Demand Prediction
#### 2.1.3 Spatial Statistics in Demand Prediction  
Spatial statistics has long been a cornerstone in understanding and predicting spatially distributed phenomena, including product demand. Traditional spatial statistical methods have provided valuable insights into the geographic aspects of demand prediction, forming a foundation upon which more advanced techniques like our SGNN approach build. Spatial autoregressive (SAR) models [13] have been widely used in econometrics and marketing to capture spatial dependencies in demand patterns. These models assume that the demandin one location is influenced by the demand in neighboring locations, an idea that aligns with our graph-based approach. However, SAR models typically rely on predefined spatial weight matrices and may struggle with non-linear spatial relationships. Geographically Weighted Regression (GWR) [14] extends traditional regression by allowing coefficients to vary over space, considering that the impact of predictors on demand may differ across locations. While GWR provides localized insights, it may not fully capture the complex, non-linear spatial interactions that our SGNN model aims to address. Kriging, or Gaussian process regression [15], has been applied in geostatistics to predict values at unobserved locations based on observed data points. This method excels in interpolation but may face challenges in extrapolation and handling the high-dimensional feature spaces that are common in e-commerce data. Our SGNN approach builds upon these foundational spatial statistical methods by leveraging the flexibility of Graph Neural Networks. It allows for learnable, non-linear spatial relationships and can incorporate high-dimensional feature spaces more naturally. Moreover, the attention mechanism in our model provides a data-driven alternative to the fixed spatial weights used in traditional spatial statistics, potentially capturing more nuanced spatial dependencies in demand patterns.
## 2 Methods
### About Earlier Validation Index Comparisons  
There has been a lot of research on cluster validation, including comparisons of different validation indices and clustering algorithms. Often when a new index is proposed, the work also includes a set of comparisons that conclude that the new index is the best one. In [34], eight common CVIs were compared and with 5% additional noise, different densities, and skewed distributions, most indices were able to find the correct number of clusters. However, only three of them were able to recognize close subclusters. In their tests, S_Dbw was the only CVI that suggested the correct number of clusters for all datasets. In our previous tests in [30], the S_Dbw also recognized the close subclusters in Sim5D2, but it did not perform that well in general.  
Often no single CVI has a clear advantage in every context, but each is best suited to a certain kind of data. This was also the conclusion in [33], where 30 different indices with 720 synthetic and 20 real datasets were compared. However, a group of about 10 indices were found to be the most recommendable, including Silhouette, Davies-Bouldin * and Calinski-Harabasz at the top. Also, in the earlier extensive comparison [47], where 30 indices were compared, the authors suggested that if different datasets were used for testing, the order of the indices would change but the best ones--including Calinski-Harabasz, Duda-Hart, and the C-index--would still perform well.
# Improvement of 5G Transportation Services with SDN-Based Security Solutions and beyond 5G
## 5 Transport Management with SDN
### Intelligent Security Solutions  
Security solutions need to be proactive because ITS set up the necessary protection services and facilities according to on-coming security alerts. In order to create an intelligent security solution, SDN involvement allows the transport service creators to simplify the ITS configurations without losing legacy systems and software.  
Table 3 provides the tentative security solutions for securing the ITS when the error (e) changes with different data (\(\mathbf{P_{u}}\) or \(\mathbf{L_{u}}\) or \(\mathbf{E_{u}}\) or \(\mathbf{S_{u}}\)) considered in the traditional and SDN environment. Using (1) and (2), security, which is proportional to intelligence value, can be  
maintained for different ITS levels. When we considered the different ADVs, the security increased with decreasing error. Some automated functions with drivers, both automated and manually operated, condition automation within a limited time, and fully self-driving cases are considered in four ITS levels, respectively.  
Table 3 is completed for managing the data traffic related to the transport services. Intelligent security solutions of ADV rely on predictions and proactive notes which depend on future security attacks. To maintain transport management, intelligent security solutions should be designed with the evolving VCS and emerging technology. When an error is decreased, the best intelligence can be maintained proactively. Still, it is a trade-off with the EE because it will be very expensive to prevent or detect attacks intelligently and proactively. The design complexity of intelligent security solutions and the cost of energy consumptions will be very high.
# Path Loss Prediction Based on Machine Learning: Principle, Method, and Data Expansion
## 5 Opportunities for Further Research
### Incremental Learning  
Until now, machine-learning-based algorithms for path loss prediction are almost based on batch learning, which assumes that all training samples are available before the training. After learning from these samples, the training process is terminated and the model building is finished. However, in practical applications, training samples of path loss increase gradually over time. After new samples arrive, relearning process with all the data takes considerable amount of time and space.  
Incremental learning algorithms can gradually update, correct, and enhance previous knowledge so that the updated one can adapt to new arriving samples without relearning from all the data. New knowledge can be learned from the new data to build a more accurate path loss predictor, whereas most of the previous-learned knowledge is retained. However, the accuracy of the path loss predictor may be negatively affected by introducing incremental learning algorithms, which lack the forgetting mechanism for selecting training data.
# Fault Location Method of Multi-Terminal Transmission Line Based on Fault Branch Judgment Matrix
## 4 Research on Multi-Branch Line Fault Ranging Technology Based on Fault Branch Judgment Matrix  
### T-Type Transmission Line Fault Branch Judgment Method  
When the T-type transmission line in Figure 4 experiences a failure, and the time for the fault traveling wave to reach each endpoint is \(t_{M1}\), \(t_{M2}\), \(t_{N1}\).  
There are two cases of faults on T-type transmission lines: faults on branch lines and faults on T nodes. These are discussed separately in this paper.
# Efficient Data Exchange between WebAssembly Modules
## 5 Results and Discussion
### Generated Code
#### 5.2.2 Performance  
In initial tests, even without any use of shared memories, Karmem demonstrates superior performance to JSON and FlatBuffers. The size of the generated WASM is also slightly smaller compared to FlatBuffers, even with excessive use of inlining. Table 5 shows the performance comparison for writing data (Figures 33 and 34), assuming different sizes for the internal vector.  
Figure 33: Example of serialization with FlatBuffers.  
Figure 32: Description of structure in Karmem.  
1. func ToKarmem(data data.TemperatureBatch) []byte {
2. builder:= fn.NewSegmentRetained(1_000_000)
3. reader:= builder.Reader()
4.
5. // Create the batch
6. batch, ok:= fn.NewTemperatureBatchWriter($reader)
7. if lok {
8. panic(\"not ok\")
9. }
10.
11. // Create an array of readings
12. readings, ok:= batch.Readings(Greader, uint32(len(data.Readings)))
13. if lok {
14. panic(\"not ok\")
15. }
16.
17. // Create temperature readings in Karemem
18. timestamps, ok:= readings.Timestamp($reader)
19. if lok {
20. panic(\"not ok\")
21. }
22. values, ok:= readings.Value($reader)
23. if lok {
24. panic(\"not ok\")
25. }
26.
27. for i:= range data.Readings {
28. timestamps[i] = data.Readings[i].Timestamp
29. values[i] = data.Readings[i].Value
30. }
31. return builder.Bytes()
33. }  
The WASM module should also read and process data efficiently, and Karmem enables this in two ways: a copy-free reading format and memory sharing. Additionally, a performance advantage arises from the reduced indirection required due to the SoA layout. The code generated by Karmem can be manipulated as native homogenous arrays, allowing the compiler to easily optimize it. Table 6 shows the comparison of data-processing time described in Figures 31 and 32. The comparison with JSON was excluded because TinyGo has additional host resources to use JSON.  
The code generated by Karmem is extensive, consisting of 1286 lines, while FlatBuffers has 254 lines but requires an external library to function and is thus not self-contained. The size of the compiled WASM file is also different, with Karmem being the smallest. This is unexpected considering that the code contains various inlining and duplications. The module using Karmem is 8.7 KB, while FlatBuffers is 9.1 KB, and JSON requires 262 KB. JSON uses runtime reflection techniques, which make it not only inefficient but also significantly larger.  
Karmem achieves better performance, even with the use of bound checking, which is not present in FlatBuffers. Additionally, all structures used in the example are dynamic, which corresponds exactly with the FlatBuffers table. Preliminary tests indicate that adding new fields to an existing struct does not significantly impact performance, as these additional fields can be entirely ignored by the reader.
## 3 The Advantages and Disadvantages of Lasers over Microwaves in Deep Space Operations
A polar orbit would provide maximum information and coverage. In this instance, the impact of the planetary atmosphere on the interplanetary lasercom link is eliminated, and the plane of the polar orbit can be oriented to provide uninterrupted visibility to the Earth station(s) for long periods of time. Nevertheless, the polar orbit adds yet another planetary terminal motion that must be accounted for in pointing the Earth telescope, and vice versa. All of these issues will be addressed in later sections. Finally, as with any new technology, lasers have a somewhat limited space heritage, lifetime, and reliability compared to more mature but data-limited microwave systems. However, several Q-switched Nd:YAG (\(\lambda\) = 1.06 \(\upmu\)) multinational laser altimeters have already operated for years in orbits about the Earth, Moon, Mars, Mercury, and occasional asteroids, and several are planned to operate in orbit about planetary moons. Although most have operated at low repetition rates (<40 pulses per second), NASA's ICESat-2, launched in 2018, operates in single photon mode with six beams from a single Q-switched Nd:YAG laser operating at a 10 KHz pulse rate, resulting in 60,000 altimetry measurements \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \multirow{2}{*}{**Planet Name**} & **Solar Distance (AU)** & **Planet (km)** & **Planet Orbital (Kron)** & **Planet Rotational (Kron)** & **Mean Field (Kron)** & **Mean Field (Kron)** & **Mean Field (Kron)** & **Mean Field Angular Distance (Kron)** & **Mean Field Angular Radius (trad)** \\  & **Solar Distance (AU)** & **Planet (km)** & **Period (Kron)** & **Speed (Kron/h)** & **Distance (AU)** & **Time (min)** & **Mean Field (trad)** & **Mean Field Angular Radius (trad)** \\ \hline
**Mercury** & 0.38 & 4879 & 0.2 & 10.83 & 1 & 8.3 & 16.42 & 40.93 \\ \hline
**Venus** & 0.72 & 12,104 & 0.6 & 6.52 & 1 & 8.3 & 40.73 & 42.93 \\ \hline
**Earth** & 1.00 & 12,758 & 1.0 & 1574 & 0 & 0 & NA & NA \\ \hline
**Mars** & 1.52 & 6792 & 1.9 & 866 & 1.52 & 12.6 & 15.04 & 28.24 \\ \hline
**Jupiter** & 5.2 & 142,984 & 11.9 & 45,583 & 5.2 & 43.0 & 92.52 & 8.25 \\ \hline
**Saturn** & 9.54 & 120,536 & 29.5 & 36,840 & 9.54 & 78.8 & 42.51 & 4.50 \\ \hline
**Uranus** & 19.2 & 51,118 & 84 & 14,794 & 19.2 & 158.6 & 8.96 & 2.24 \\ \hline
**Neptune** & 30.1 & 48,528 & 164.8 & 9719 & 30.1 & 248.3 & 5.43 & 1.43 \\ \hline
**Pluto** & 39.5 & 2376 & 248 & 47.18 & 39.5 & 326.3 & 0.20 & 1.09 \\ \hline \end{tabular}
\end{table} 
Table 1: Planetary parameters relevant to the acquisition and tracking of the opposite terminal during wideband communications between Earth and the planet under investigation. The angular radii for Earth and the planet it is communicating with corresponds to the angular spread seen by the opposite terminal at the average interplanetary distance. The actual distance can vary between \(\text{RPE}_{\text{ave}}+/-1\,\text{AU}\). per second from a 500 km Earth orbit. Unfortunately, none of these low repetition rate, space-qualified lasers are suited for wideband (MHz to GHz) laser communications.
###### Abstract  
Monitoring vast and remote areas like forests using Wireless Sensor Networks (WSNs) presents significant challenges, such as limited energy resources and signal attenuation over long distances due to natural obstacles. Traditional solutions often require extensive infrastructure, which is impractical in such environments. To address these limitations, we introduce the \"FloatingBlue\" architecture. This architecture, known for its superior energy efficiency, combines Bluetooth Low Energy (BLE) technology with Delay Tolerant Networks (DTN) and data mules. It leverages BLE's low power consumption for energy-efficient sensor broadcasts while utilizing DTN-enabled data mules to collect data from dispersed sensors without constant network connectivity. Deployed in a remote agricultural area in the Amazon region, \"FloatingBlue\" demonstrated significant improvements in energy efficiency and communication range, with a high Packet Delivery Ratio (PDR). The developed BLE beacon sensor achieved state-of-the-art energy consumption levels, using only 2.25 \(\upmu\)l in sleep mode and 11.8 \(\upmu\)l in transmission mode. Our results highlight \"FloatingBlue\" as a robust, low-power solution for remote monitoring in challenging environments, offering an energy-efficient and scalable alternative to traditional WSN approaches.  
IoT; DTN; BLE; UAV; low power communications +
Footnote †: journal: Sensors
## 1 Introduction
The factors influencing the decontamination effect of the BSF reactor include filter material type, hydraulic load, temperature, oxygen levels, filtration rate, and filter layer thickness. Among these, the characteristics of the filter material are crucial for determining the decontamination effect of the BSF reactor [11]. Researchers have extensively investigated filter material characteristics to enhance pollutant removal efficiency, including modifying existing filter materials, developing new types, and layering different filter materials. [PERSON] et al. selected quartz sand and activated carbon as filter materials to treat turbidity, NH\({}_{3}\)-N, chemical oxygen demand of manganese (COD\({}_{\text{Mn}}\)), UV absorbance measurements at 254 nm (UV\({}_{254}\)), and TOC in slightly polluted lake water. Their results indicated that after biofilm formation, the pollutant removal rates of activated carbon surpassed those of quartz sand, reaching 92.18%, 65.53%, over 70%, and 60.99%, respectively [12]. [PERSON] et al. used quartz sand of varying particle sizes as filter materials to remove turbidity, NH\({}_{3}\)-N, and dissolved organic carbon (DOC) from drinking water, achieving, after biofilm formation was completed, average pollutant removal rates of up to 93.42%, 97.25%, and 53.7%, respectively [8]. [PERSON] et al. employed fine sand, fine gravel, and coarse gravel as filter materials. By layering different filter materials, they achieved efficient Escherichia coli removal at a rate of 95% through layered configurations [13]. [PERSON] designed a sand filter and an activated carbon filter device, connecting the outlet of the sand filter to the inlet of the activated carbon filter to operate in series, achieving a chemical oxygen demand (COD) removal rate of 89.5% [14]. [PERSON] et al. improved zeolite to enhance turbidity, UV\({}_{254}\), COD\({}_{\text{Mn}}\), NH\({}_{3}\)-N, and TP removal effects. [PERSON] et al. [15] modified natural zeolite (Z-Na-Fe) with polymeric ferric sulfate activated by sodium chloride to improve the removal effects of NH\({}_{3}\)-N and phosphorus in cell water, achieving removal rates of 82.81% and 77.30%, respectively [16]. Most of the aforementioned research has concentrated on the removal performance of single filter materials, while studies on the decontamination performance of mixed filter materials remain relatively scarce. This study employs volcanic rock and coconut shell activated carbon with the same particle size range as filter materials to investigate the performance of a BSF reactor in removing slightly polluted rainwater under both single and mixed filter material conditions.
## 5 Experiments and Discussion
### Computational Performance Analysis
Given that the computational process involves trigonometric evaluations, further improvements in execution speed may be achieved in practical engineering applications through fixed-point conversion or lookup-table optimization techniques. Based on typical optimization strategies in embedded implementations, the execution time can potentially be reduced by a factor of two to three, depending on the use of fixed-point arithmetic or lookup-table techniques. These optimization techniques are expected to further improve the real-time capability and computational efficiency of the proposed method.
# Computer Vision Based Planogram Compliance Evaluation
## 3 Experiments and Analysis of the Results  
### Datasets  
We utilize the SKU-110K product proposal generation dataset [21] for training and testing the product proposal generator, and the GP-180 product detection dataset [13] for training and testing the classifier. We select these datasets due to the fact that they are the most suitable openly available datasets for the product proposal generation and product classification problems, respectively. The SKU-110K dataset provides 11,762 images with more than 1.7 million annotated bounding boxes and GP-180 instance-level annotations of 74 rack images. For more details on these datasets, the reader is encouraged to refer to the original papers.  
The only public dataset for planogram compliance evaluation that we know of is included in GP-180. However, this dataset is problematic in several ways. First, the planograms in GP-180 consist only of information on the position of products rel event to each other instead of Cartesian coordinates in reference to the shelf--for example, a planogram might say that Product A should be above Product B, and that Product C should be on the right side of product B. This means that in order to use the proposed RANSAC matching, we would first need to somehow infer the Cartesian coordinates of the products based on these relative positions, which is prone to error. Second, in GP-180, all images are fully planogram compliant--a predictor that consistently gives 1 as the planogram compliance would achieve perfect accuracy with GP-180. Third, as already outlined in Section 2.3, the planograms in GP-180 encompass only small fractions of whole shelves. This makes the planograms both unrealistic and enables solving the problem with methods that are unfeasible with a realistic amount of facings and different products per image. The smallness of planograms in GP-180 also makes it unsuitable for our RANSAC-based matching, which benefits from a larger number and diversity of products that can be found in real whole-shelf images.  
Due to these limitations, and in order to test the performance of our approach with actual production data, we collected an internal dataset with the help of a large retailer. The dataset consists of five test pictures encompassing whole racks in the vein of SKU-110K, corresponding planograms and iconic images of 7290 products. The test pictures were not annotated, but their planogram compliance was known.  
For each of the products, there could be up to six images for the six facings (front, back, top, bottom, left, and right) and additional images for different merchandising types such as product trays. Due to this, there were a total of 27,204 product images, on average 3.7 images per product.  
The product image data also included a four-level product group hierarchy in the vein of GP-180. We extended this hierarchy with two additional levels: the facing of the product and the merchandising type.