esa-sceva/satcom-chunk-collection huggingface dataset:
# Sigfox and LoRaWAN Datasets for Fingerprint Localization in Large Urban and Rural Areas
## 5 Results  
To evaluate the usability of our LPWAN datasets, we have executed the fingerprinting algorithm that was described in Section 4 on all three datasets. Every dataset was randomly split up in a training subset, an evaluation subset and a test subset; the sizes of these subsets are 70%, 15% and 15% of the total size of the complete set. The evaluation subsets were used to find the optimal parameters for each LPWAN dataset. A parameter sweep was conducted, varying \(k\) from 1 to 15. We decided upon an optimal value for \(k\) per dataset by calculating which \(k\) resulted in the lowest mean location estimation error. This optimal \(k\) was then validated with the test subset, as described in steps 6 and 7 of the algorithm in Figure 9. The results of this validation are shown in the logarithmic box plots of Figure 10, as well as in Table 5.  
The rural Sigfox dataset has a mean error of 214.58 m and a median error of 15.4 m when looking for the single nearest neighbor, whereas the urban Sigfox dataset has a mean error of 688.97 m and a median error of 514.83 m when \(k\) equals 10. Lastly, the urban LoRaWAN dataset has a mean error of 398.4 m and a median error of 273.03 m when using the 11 nearest neighbors to get a location estimate. We will discuss these results in the next section.
## Literature Review
### Frameworks for Topic Discovery (Interest Mining)  
As far as topic discovery (or user interest mining) is concerned, the work [24] proposed a framework for \"Tweets Classification, Hashtags Suggestion and Tweet Linking\". The framework performs seven activities: (i) data-set selection; (ii) pre-processing of data-set; (iii) separation of hashtags; (iv) finding relevant domain of tweets; (v) suggestion of possible interesting hashtags; (vi) indexing of tweets; (vii) linking of tweets. Thus, topics are represented by hashtags, that are suggested to users. With respect to our approach, discovered topics are very fine grained (at the level of hashtags), because the idea is to suggest hashtags to follow, not users.  
In a similar study [25], to detect user interests by automatically categorizing data on the basis of data collected from Twitter and Reddit, authors proposed a methodology comprised of two steps. (i) multi-label text classification model by using Word2 vec [26], a predictive model and (ii) topic detection by using Latent Dirichlet Allocation (LDA) [27], a statistical topic detection model based on counting word frequency from a set of documents. A pool of 42,100 documents collected from Redit and manually labeled was used to train the model; then, a pool of 1,573,000 tweets (posted by 1573 users) was used as training set. This work is interesting because it uses Reddit to build the classification model to classify unlabeled tweets from Twitter. However, the scenario is quite different with respect to our paper: in fact, we propose that users wishing to be influencers voluntarily label their posts, with the goal to be recognized as influencers.  
The work [28] presents a web-based application to classify tweets into predefined categories of interest. These classes are related to health, music, sport, and technology. The system performs various activities. First of all, they fetch tweets from Twitter and pre-process them; second of all, feature selection from texts is performed; finally, the machine learning algorithm is applied. Although, from a general point of view, it is an interesting system, it is designed to perform analysis of messages from outside the micro-blog. In contrast, our goal is to find out the best technique suitable to discover topics within the micro-blog application.  
So, we can say that our envisioned application scenario is quite novel; furthermore, the specific goal of the investigation framework presented in this paper is not to be the end-user solution, but a tool to discover the technique that is most suitable to be executed within the micro-blog application to discover topics.
# No-CH4-SCR Over Core-Shell MnH-Zeolite Composites
## 2 Materials and Methods  
### Catalyst Preparation  
Zeolite composite was synthesized in the laboratory by two-step hydrothermal crystallization with sodium silicate aqueous solution (29.12% SiO\({}_{2}\), [OH\({}^{-}\)] = 2.75 mol/L), colloidal silica ([SiO\({}_{2}\)] = 6.02 mol/L), sodium aluminate aqueous solution (31.65% Al\({}_{2}\)O\({}_{3}\), [OH\({}^{-}\)] = 9.87 mol/L), sodium hydroxide, tetraethylammonium bromide, ammonium hydroxide aqueous solution (25% NH\({}_{3}\)), and distilled water. The synthesis procedure was as follows: Na-Y zeolite was first synthesized in terms of the composition of (2.0-2.5) Na\({}_{2}\)O:(4.0-6.0) SiO\({}_{2}\):Al\({}_{2}\)O\({}_{3}\):420H\({}_{2}\)O. The synthesis gel was autoclaved at 373 K for 24 h. Then, it was cooled down to room temperature and added to the synthesis gel of zeolite Beta under strong stirring conditions. The synthesis gel of zeolite Beta was prepared with tetraethylammonium bromide, NH\({}_{3}\)-H\({}_{2}\)O, colloidal silica, and sodium aluminate aqueous solution by following the procedures established by [PERSON] and co-workers [23]. The pH value of the above mixture was adjusted to 12.0-12.5 with concentrated H\({}_{2}\)SO\({}_{4}\) acid. Finally, the resultant uniform mixture containing zeolite Y was crystallized again at 413 K for 120-160 h in a stainless-steel autoclave. The products were filtered, washed, and dried at 393 K for 8 h to obtain core-shell zeolite composite, denoted as ZZs-n (n is the ratio of zeolite Y and zeolite Beta in the composites). Some samples were further calcined at 823 K for 6 h in an air flow with a heating rate of 1 K/min. Na-Y zeolite was synthesized according to the same method, whereas Na-Beta zeolite with a Si/Al ratio of 15 was synthesized in the laboratory.  
The calcined samples were first ion-exchanged with 0.1 mol\(\cdot\)L\({}^{-1}\) of aqueous solution of NH\({}_{4}\)NO\({}_{3}\) at a liquid to solid ratio of 20 mL/g. The solid products were then thoroughly washed with distilled water, dried at 383 K overnight, and finally calcined at 823 K to get H-zeolites. H-zeolites were subsequently ion-exchanged with aqueous solution of Mn(CH\({}_{3}\)COO)\({}_{2}\). The obtained solid samples were washed with distilled water, dried at 383 K overnight, and calcined in air flow at a ramp of 2 K\(\cdot\)min\({}^{-1}\) from room temperature (RT) to 823 K and kept for three hours to obtain MnH-ZZs-n catalysts. MnNa-ZZs-n catalysts were acquired using the same process. The ion-exchange conditions and selected properties of the catalysts were listed in Table 1. With a decrease of zeolite Y in the zeolite composites, the special surface areas of the composites decreased because of a larger surface area of zeolite Y than zeolite Beta.
## 3 Research Methodology
### Experimental Analysis and Presentation of the Result  
The performance of the proposed approach has been assessed using four performance evaluators, derived from the confusion of the matrix containing the following components: true positives (TPs), true negatives (TNs), false positives (FPs), and false negatives (FNs). Hence, the performance metrics Precision, Recall, F1-score, and Accuracy (Acc) are formulated (see Table 2). The \"Accuracy metric\", denoted as ACC, assesses the overall performance of the model. The \"Recall Metric\" is computed via dividing the count of TPs by the sum of TPs and FNs. The \"Precision Metric\" is a quantitative measure that evaluates the ratio of accurate positive predictions to the overall number of positive class values predicted [46]. The \"F1-score\" is a quantitative measure that evaluates the precision of a classifier. The \"False Positive Rate (FPR)\" quantifies the ratio of negative instances that are erroneously classified as positive by the model. Put simply, it measures the frequency at which the model commits errors through incorrectly predicting a positive outcome when the true label is negative [47; 48].  
### Experimental Analysis and Presentation of the Result  
The experimental analysis encompasses two distinct aspects: the benchmarking dataset and the dataset generated through this research. Nevertheless, the experimental implementation employs identical methodology that entails the fusion of convolutional neural networks (CNNs) and long short-term memory (LSTM).
## 4 Results and Discussion
### Comparison with Other Methods  
In this section, based on the results presented so far, we compare the MA, R-R, and V2 GSmooth methods in terms of their performance for power smoothing. The proposed method, V2 GSmooth, demonstrated higher power smoothing, effectively reducing the variability of renewable power delivered to the grid. It achieves a smoother power profile, resulting in a more stable power injection to the distribution network. On the other hand, the MA and R-R methods performed better in terms of delivering energy to the system. They provided more energy, making them more advantageous in terms of power availability. However, they may have exhibited slightly less power smoothing than the V2 GSmooth method. The MA and R-R methods are more straightforward to implement in software as they are based on non-complex linear algorithms. This is an advantage over the V2 GSmooth method, where implementing fuzzy logic control leads to a higher computational burden during execution.  
Overall, the choice between the MA, R-R, and V2 GSmooth methods depends on the specific requirements and priorities of the RES-EV system. If the primary goal is to mitigate power fluctuations and ensure a consistent power output to the grid, the V2 GSmooth method is highly recommended. Conversely, the MA and R-R methods may be more suitable if the emphasis is on maximizing energy delivery.
## 4 Emerging Lightweight Security Approaches
### Asymmetric Encryption--Lightweight ECC Approaches
#### 4.2.3 ECC Applications in IoT  
As with many other public-key algorithms, ECC is used with symmetric encryption algorithms to make a hybrid crypto-system. For example, ECDH helps to generate and deliver the session key for a symmetric key algorithm, which will be used by the sender and receiver to encrypt the data packets [99]. ECDH is one of the many key bootstrapping procedures described in [18], which reviewed nine algorithms that incorporated ECC as the public-key algorithm.  
An example of ECC applications can be found in the e-health record network (EHR). EHR requires strong security protocols to protect sensitive patient data. A proposed EHR security solution is the lightweight distributed access control system with keyword search (LDAC-KS) [100]. ECC was chosen in this system because it provides security equivalent to RSA but with smaller key sizes.  
Because of its lightweight nature and strong security, ECC has been adapted to create other protocols [85], which aim to make ECC accessible for IoT devices. Below are some examples, which have also been summarized in Table 7.  
* **ECC Watermark:** This is a modified version of the ECC algorithm, where the ECDH key exchange protocol is combined with a new authentication method. ECC Watermark replaces ECDSA with a fragile zero watermarking technique [84]. The watermark is generated during the encryption phase by the sender, who generates a hash of the packet's source IP address, protocol field, and destination IP address using the sender's private key. The generated watermark is appended to the packet's payload. This method utilizes a private-key hash function rather than elliptic curve multiplication operations for signature generation, thus avoiding the complexity of the multiplicative inverse operation. This can be considered a lightweight hash with a smaller output, which is the watermark that is appended to the packet.
* **ECIOT**: Elliptic Curve Internet of Things (ECIOT) [85] utilizes the ECDH key exchange protocol so that the sender and receiver can establish a session key \(K\). The ECIOT protocol then performs an XOR operation using as inputs \(K\) and the data. In summary, ECIOT encrypts and decrypts data with the simple XOR operation, after ECDH generates the shared key \(K\). This can be considered a simpler round of encryption, where one logical operation constitutes one round of the encryption process.  
* **Lightweight Elliptic Curve Accelerator**: The work proposed in [86] is the hardware architecture of an accelerator that performs the scalar multiplication that generates the public key of ECDH. The private key \(a\) multiplies the base point \(G\) to generate the public key \(\mathrm{H_{a}}=aG\). This means that the base point \(G\) is added a number of times. The design goal of the proposed hardware accelerator is to reduce the hardware footprint, or area of the FPGA, to implement this scalar multiplication efficiently.
## 2 Blockchain: An Overview
### Key Components of Blockchain
#### 2.1.2 Immutable Records (Transactions)  
Transactions, or immutable records, are the fundamental units of information exchanged within a blockchain network. When a transaction is completed and added to the shared ledger, it becomes immutable, ensuring that the recorded information remains tamper-proof. This immutability is central to establishing trust among network participants.
# Collision Avoidance Resource Allocation for LoRaWAN
## 7 Proof-of-Concept
This time is composed of a fixed part (to avoid sending messages too fast) and a random part (to avoid correlation between transmissions from different nodes). * For transmission, a **border effect check** has been implemented based on the time to initiate transmission and the ToA calculation (see Equation (2)). If the transmission ended on the next time window, it would be postponed until the beginning of that window. Figure 13: CARA architecture. Figure 14 shows our testbed, which is composed of 30 nodes, one gateway and one server. The server implements both the Chirpstack platform and our CARA server. An example of the messages exchanged between one node and the CARA server is also provided. As shown, the CARA server receives one \"#JOINREQ#\" message (_JoinRequest_), computes the initial resource block to be assigned to the node, and sends back this value in the \"#JOINACC#\" (_JoinAccept_) message. In addition, an spreading factor mask is also sent, which indicates which SFs are available to the node (one bit per SF, so 63 means that all the six SFs are available). In this experiment we have assumed that all spreading factors are available to all nodes. Data transmissions from each node are sent after \(10+rand(10)\) s, where the \(rand(x)\) function returns a random value between 0 and \(x\). This produces a network load of 6000 packets/s. As in our simulations, data frames contain 24 bytes. We have verified that, since the number of devices is smaller than the number of resource blocks, each device is granted a different initial resource block and therefore no collisions occur. Figure 15 shows an example of RBs allocation to the nodes on our testbed. Only 5 nodes are shown for the sake of clarity. As expected, nodes do not collide (residual collision probability lower than 0.5% due to other LoRaWAN devices in the surroundings) and the RBs are allocated sequentially to each node, following a ramp pattern. In addition to the CARA server log, we also checked the correct behaviour of our solution analyzing the traces from the node (in the Pycom console) and the frames received by the Chirpstack application server. Figure 14. Testbed with our CARA implementation.
# The UWB Radar Application in the Aviation Security Systems
## 3 Results  
### Results of the Application of the Straight-Line Method  
The above procedure of localization of the target by the method of lines was applied to the real data ER_TU175_IL43. In order to determine the positions of the moving person, two independent UWB radar systems that use a pseudo-random sequence M were applied. The target was a person moving from position P (1), through positions P (2), P (3), P (4), again to position P (1).  
The results of determining the position of a moving person are shown in Figure 13. The x and y coordinates in Figures 13 and 14 are given in m. The results of processing measuring signals UWB radars showed that the created algorithms are suitable for locating the movement of a person behind an obstacle. From Figure 13, it is clear that the accuracy of identification is not very high, but it is sufficient for the purpose of identifying the person behind the obstacle. If a person moved from point P (1) to point P (2), his position would be determined with sufficient accuracy. When moving a person from point P (2) to point P (3), the errors in determining the position of the person were relatively large, but the determined trajectory of the person's movement was in accordance with the person's movement behind the obstacle. There are errors in the specified trajectory of the person's movement between points P (2) and P (3). When a person's position is determined in front of an obstacle, similar results are obtained when determining the position of a person in moving from point P (3) to point P (4). Figure 13 shows the parts of the person's movement not detected by radar when persons move from position P (4) to position P (1). To increase the accuracy of localization, we used the Kalman filter when processing the  
Figure 12: Examples of center of gravity point positions for (**a**) \(\tau=50\) and (**b**) \(\tau=100\).  
UWB radar signal by the above method. The result of the target localization procedure after the application of the linear Kalman filter is shown in Figure 14.  
The results of determining the position of a person moving behind an obstacle after applying the linear Kalman filter show that the positioning accuracy has improved. Even in this case, the radar completely did not detect the movement of a person from position P (4) to the position P (1).
# Multi-Feature-Enhanced Academic Paper Recommendation Model with Knowledge Graph
## 2 Related Work
### Graph-Based Academic Paper Recommendation Methods  
Recently, graph-based academic paper recommendation methods have also garnered the interest of researchers. Graphs, as a structured form of knowledge representation, can effectively express the knowledge structure and relationships within academic fields. Some researchers have proposed methods for constructing academic paper recommendation systems using graphs, aiming to achieve more accurate and personalized recommendations by mining academic entities and relationships within the graph. For example, [PERSON] et al. [17] proposed a heterogeneous graph representation-based recommendation method called HGRec, which uses two meta-path-based proximity measures to jointly update the node embeddings in a heterogeneous graph. [PERSON] et al. [18] introduced a bibliographic network representation (BNR) model that integrates the bibliographic network structures and content of different entities (authors, papers, and venues) for efficient recommendation. [PERSON] et al. [19] presented a keyword-driven and popularity-aware paper recommendation method based on an undirected paper citation graph. [PERSON] et al. [20] constructed a heterogeneous graph to represent citation and content information in papers and then applied a graph-based similarity learning algorithm for the paper recommendation task. [PERSON] et al. [21] proposed a method based on the periodic interests of authors and the academic graph network structure to obtain as much valid information as possible for paper recommendation. However, these graph-based methods also have some limitations. For example, processing large-scale graphs may be constrained by computational and storage resources. The complexity of graph structures can lead to decreased algorithmic efficiency. The quality and completeness of graph data may affect the accuracy of recommendation results, and some useful information may not be fully utilized.
## 2 Study Area and Datasets
### Satellite Data
#### 2.3.4 Soil Surface Wetness from Scatterometer Data  
Precipitation records are only available from Mare Sale at 100 km distance. Soil moisture records have been also only collected occasionally on central Yamal, (e.g., [36]). C-band backscatter can provide an alternative account for near surface wetness [45]. Variations are expected to reflect surface wetness changes under unfrozen conditions. This would allow obtaining of information from moisture conditions before freeze-up what is expected to be of relevance for subsidence in the following spring. There are however no Sentinel-1 acquisitions before 2016. An alternative is scatterometer records. The resolution is in the order of 25 km, but nearly daily records are available. C-band data are available from the ASCAT (Advanced scatterometer) instrument onboard the Metop satellites. The backscatter data used in this study is provided by EUMETSAT (European Organisation for the Exploitation of Meteorological Satellites) in a 12.5 km grid as part of the soil moisture data product (evaluation example in tundra [46]). Data is continuously available since 2007, providing global coverage with a temporal resolution of approximately daily observations. The backscatter records used in this analysis are provided normalized to a 40\({}^{\circ}\) incidence angle. Comparisons are limited to the original backscatter (rather than derived soil moisture products) due to known deteriorating effects of tundra specific issues  
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Date** & **Day of Year** & **Degree-Days of Thawing** & **Date** & **Day of Year** & **Degree-Days of Thawing** \\ \hline  
ctober 2013 & 280 & 739.8 & & & \\
10 October 2013 & 284 & 725.4 & & & \\ \hline \hline \end{tabular}
\end{table}
Table 2: COSMO-Skymed acquisition dates and corresponding air temperature derivatives (station Mare Sale).  
on the parameterization of soil moisture retrieval [47; 48]. [PERSON] et al. [46], however, demonstrated that ASCAT derived near surface soil moisture correlates comparably well with in situ measurements over tundra in autumn, specifically over the last 20 days before freeze-up.  
The selected ASCAT grid cell (center 70.278\({}^{\circ}\)N, 69.008\({}^{\circ}\)E) covers all in situ measurement sites. ASCAT data as well as precipitation records from Mare Sale are used for the discussion of soil moisture influence on the results of the subsidence and active-layer thickness analyses.
## Appendix A Deforming Element
### Investigation of the Effect of SB Basic and Additional Parameters  
The relative share of research on the effect of individual basic and additional parameters (governing factors) of the SB process is shown in Figure 8. The most frequently studied parameter is burnishing force (33%), followed by feed rate (26%), and burnishing velocity (18%). It is noteworthy that the influence of the radius is the least studied of the basic parameters, despite the fact that some studies show that this parameter is of primary importance. For example, in [69], it was found that the influence of the radius on the fatigue limit of DBed 41 Cr4 steel is almost twice that of the burnishing force. [PERSON] et al. [76] found that the influence of the radius on the roughness and micro-hardness of DBed Cu-8 Al-3 Fe aluminium bronze is significantly greater compared to that of burnishing force and feed rate. Of the additional parameters, the most researched is number of tool passes (8%), and the least is the working scheme (1%). The last one is puzzling, considering that the working scheme makes sense precisely for multi-pass SB. In general, the additional parameters have been studied significantly less often compared to the basic, despite the fact that they are essential in some cases [100].  
Figure 8: Percentage share of the research on the effect of the main and additional SB parameters.  
Figure 7: Percentage share of the types of lubrication and friction conditions.