esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Evaluation
### Results and Discussion
#### 6.2.7 Comparison of the MM-LDA to Conventional Methods  
The proposed method is compared to other conventional methods stated in Section 2 to show the effectiveness of the proposed method. Figure 11b shows the comparison results when the proposed method is used compared to various conventional machine learning classifiers. The cause of scan failure/delay of various scenarios in the second set of scan response samples obtained through simulation is estimated using the trained model explained in Section 6.2.5. According to the results, we confirm that the proposed MM-LDA with filtered training set outperforms other conventional machine learning classifiers with an average of 98% correct estimation of various causes of scan failure/delay. Although the conventional LDA is able to estimate normal scan response situation and failure/delay caused by NW congestion correctly with 90% correct estimation, it fails to estimate failure/delay caused by SNR insufficient, with only 17% of the sample estimated correctly. Similarly, the CART classifier, which is able to estimate the cause of scan failure/delay correctly in both training and test samples, fails to generalize the new scan response sample. Only the failure/delay caused by NW congestion is correctly estimated, with 99% estimation. In addition, NB and SVM are able to estimate normal scan response and failure/delay caused by SNR insufficient correctly, with 100% and 93% estimation, respectively. This confirms that the proposed MM-LDA with filtered training set does not overfit or underfit the training set.
## 3 Results
### Spatial Distribution Analysis of Abandoned Farmland  
The results indicate that in the study area, there are 931 patches of perennial abandoned farmland, covering an area of 83.49 \(\mathrm{hm}^{2}\), which accounts for 2.18% of the total cultivated land. In terms of seasonal abandonment, 1196 patches of abandoned farmland were identified in March 2021, covering an area of 123.10 \(\mathrm{hm}^{2}\), which accounts for 3.21% of the total cultivated land. In December 2021, 865 patches of abandoned farmland were recorded, covering an area of 454.39 \(\mathrm{hm}^{2}\), which accounts for 11.86% of the total cultivated land. The statistical results show that seasonal abandonment is most severe in December. Although the number of abandoned patches in March is higher than in December, the patches in March are more fragmented, resulting in a smaller total abandonment area compared to December. Based on the results of this study (Figure 4b), the overall spatial distribution of abandoned farmland is concentrated primarily in the southeastern part of the study area. A kernel density analysis of the abandoned farmland distribution reveals three distinct, densely concentrated core areas (Figure 5). From the perspective of core coverage, the size of these core areas varies. The largest core is located in the southeastern part of Qiaotou Township, where a large number of patches are continuously distributed. High-density areas are predominantly located near residential areas and road intersections, which are likely influenced by road density and construction activities, both contributing to the increased abandonment of farmland. In contrast, the western and northern regions exhibit  
Figure 4: Abandoned farmland distribution map. (**a**) Perennial abandonment of farmland. (**b**) Seasonal abandonment of farmland.  
a more dispersed distribution of abandoned farmland, with no notable high-density core areas, suggesting that the abandonment is more widespread and scattered in these regions.
# A Systematic Literature Review of LoRaWAN: Sensors and Applications
## 4 Discussion  
The previous works reviewed in this paper develop different applications of different categories. In this aspect, the largest percentage of the articles carry out work on monitoring the environment, for example, monitoring temperature, humidity, etc. The second largest percentage of previous works are dedicated to developing or explaining solutions from the agriculture, mobility, and health categories. What has been described above can be seen illustratively in Figure 1.  
The \"Other works\" category was composed of papers from three subcategories. The first subcategory refers to works of comparative studies; these works compare the different LPWAN technologies, as well as the different algorithms they use. Comparisons often encompass features such as range, latency, deployment costs, and device lifetimes. The second subcategory of previous works dedicates their content in general applications; these works describe the different applications that can be used in different scenarios using  
Figure 1: Categories of the solutions of the analyzed previous works.  
LoRaWAN technology. Finally, the last subcategory is made up of works that are dedicated to carrying out studies to verify if some prototypes of LoRaWAN devices comply with the performance of the market standards. The distribution of these subcategories can be seen in Figure 2.  
In addition, in Figure 3, you can see the different LoRaWAN application categories and their respective number of previous works. Likewise, it is observed that not all articles belong to a single category but rather there are some that belong to two categories.  
It is also important to indicate that in the previous works, different types of devices and tools are used, which can be categorized into (a) software, (b) boards and modules, and (c) sensors, gateways, and antennas. In Figure 4, it can be seen that the most used software is NS-3, which is used to perform LoRaWAN simulations; secondly, Matlab is used, which is also used to perform LoRaWAN simulations and additionally for data processing. After these two tools, three software tools are used most frequently: (a) OMNeT++, which is also a simulator, (b) InfluxDB, which is a database, and (c) Arduino IDE, which is a software used to program the boards.  
Figure 3: LoRaWAN application categories and their respective numbers of previous works.  
Figure 2: Distribution of the categories of “Other works”.  
Additionally, in Figure 5, you can see the brands of boards and modules that are used mostly in different projects. On the horizontal axis, you can see the brand of the different devices, and on the vertical axis, the number of previous works that use these devices.
# Progress in the Development of Electrodeposited Catalysts for Direct Liquid Fuel Cell Applications
## 4 Shape Control--A Way Forward
With the growing interest in titania and the direct synthesis of titania nanotubes (TNT) as potential catalyst support electrocatalytic applications, [PERSON] et al. [96] synthesized Pd dendrites for oxidation of formic acid. TNTs with ordered symmetry and uniform distribution obtained by anodization of bare Ti in glycol-HF electrolyte were used as the catalyst support to synthesise Pd dendrites by a facile electrodeposition route. The authors evidenced the formation of Pd dendrites on anodized TNTs and reported an enhanced catalytic performance, signifying the dominant synergetic contribution from the TNT/Ti support. Moreover, [PERSON] and his co-workers [104] performed a detailed investigation to understand the role of electrochemical techniques and its related parameters on the morphological features of Pt deposited on Ti substrates, tested them to evaluate the oxidation of methanol. The study revealed that the Pt deposition on Ti is quite dependant on the waveform applied. Pt deposited utilising the dynamic applied waveform electrodeposition techniques (cyclic voltammetry and pulse current deposition) resulted in more efficient, uniform Pt distribution on Ti relative to the constant ones (potentiostatic, galvanostatic deposition). Among the dynamic applied waveform electrodeposition conditions, Pt deposits obtained via the pulse electrodeposition technique was identified as the best choice of catalyst for methanol oxidation. The results of the investigations performed by the authors reflected the fundamental understanding of Pt depositions, their morphological characteristics at different modes of deposition (dynamic/constant waveform). Furthermore, it underlined the importance of using an ideal deposition technique and parameters to enable effective metal deposition from its metal (Pt) precursor. Table 4 summarises the recent developments associated with the Pt and Pd electrodeposition for oxidation of methanol, ethanol, and formic acid at laboratory scale. Figure 7: Scanning electron micrographs showing the morphological transitions of palladium supported on Vulcan. Reproduced with permission from [103]. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
# LoRaWAN Physical Layer-Based Attacks and Countermeasures, A Review
## 1 Introduction  
### Motivation  
Within the last six years, LoRaWAN has evolved from its first specification (v1.0, published in 2015) to a strong contender among low-power wide-area network (LPWAN) technologies. During this time frame, LoRaWAN technology has boosted connectivity for many applications, e.g., in agriculture, logistics, smart lighting and waste management. Furthermore, the popularity and the availability of the underlying LoRa modulation technology has initiated further development in research areas such as battery free back-scatter communications [1], open-source network components, e.g., the chirpstack [2], and enhanced medium-access strategies with increased throughput [3]. Recently, satellite-based LoRaWAN communication links have also been demonstrated [4], which aim to pave the way for operation without gateways, which is especially well-suited for LoRaWAN employment in rural areas.  
Despite the advances and rapidly gained popularity, the state of security in LoRaWAN networks is still inherently imbalanced. On the backend side, the servers and their interconnections can be flexibly secured and updated with the latest available software components, such as public key infrastructure, virtual private network connections, secure protocols, intrusion detection systems and firewalls [5]. Therefore, the frontend side of the network, consisting of lightweight embedded nodes, typically implements only a fragment of the available security features. Although the LoRaWAN specification gives definitions for, e.g., authenticated network join procedures and session key derivations [6], further secure development steps are left for the user to implement. Further obstacles might arise due to dissimilar attack surfaces on the backend and on the frontend. For instance, distributeddenial-of-service (DoS) and its countermeasures are well understood network security concepts. However, DoS attacks targeted at LoRaWAN nodes, e.g., via reactive RF jamming, are much harder to thwart. Finally, large scale employments of LoRaWAN with hundreds of nodes might pose security management problems on the application layer, given that a procedure for over-the-air updates for firmware patching is not defined in the current LoRaWAN specification.
## 4 Feature Description
### Typical Components and Interactions  
ACN systems typically combine a diverse range of sensors, encompassing accelerometers, gyroscopes, and in certain instances, sound, temperature, and pulse sensors. These instruments detect abrupt changes in the vehicle's dynamics, indicative of a collision or crash event. Depending on the approach proposed, a sophisticated crash detection algorithm is used, in some cases artificial intelligence can also be used in order to avoid false positives and to enhance the quality of the system. Key features and components integrated into ACN systems include:  
1. **In-vehicle communication mechanisms:** Sensors typically communicate with ECUs using CAN-bus. Depending on circumstantial evidence and data collected from sensors, some additional modules can be activated to enhance the quality of the input. Upon identifying a possible crash, the ACN system activates communication modules within the vehicle, like cellular or satellite technology. These modules help the transmission of data to external entities.
2. **Efficient data transmission:** A standardized data package dispatched to a dedicated emergency response hub, manned by trained operators. This package commonly includes vital details such as the vehicle's precise location, impact severity, and in some instances, occupant information.
3. **Sophisticated crash detection algorithms:** Utilizing advanced algorithms, the sensor data are instantaneously assessed to determine if a crash has occurred. These algorithms weigh factors like the seriousness of impact, rapid deceleration, and the collision's nature to make an accurate evaluation.
4. **Central emergency response center:** The emergency response center receives the data package and promptly dispatches suitable emergency services--ranging from paramedics to law enforcement or fire personnel--to the scene of the accident. The operators in the center can also communicate with the vehicle's occupants through an integrated communication system.
5. **User notification mechanisms:** Beyond notifying emergency services, some ACN systems may also inform the vehicle's manufacturer or a designated contact about the crash occurrence. This enables swift communication with concerned parties, including family members.
6. **Accelerated response:** ACN systems are designed to drastically diminish the time between a collision and the arrival of emergency services at the incident site. This holds critical importance in scenarios where immediate medical attention is needed.
# A Framework for Assessing the Interference from NGSO Satellite Systems to a Radio Astronomy System
## 3 Framework of the Interference Analysis
### Space-Time Distribution of Satellite EIRP
#### 3.1.1 Generating the CDFs of EIRP
Regarding the samples of \(\phi_{A}\), we drew \(U\) from the uniform distribution in \([0,1]\), then we converted them by the following equation:  
\[\phi_{A}=arccos[1-(1-cos\phi_{E})U], \tag{29}\]  
Assuming that the samples of \(\vartheta\) were denoted as \(\{\theta_{A,1},\vartheta_{A,2},\cdots,\vartheta_{A,N}\}\), by using expressions (24) and (25), we obtained the corresponding elevation and azimuth angles and removed those that did not satisfy the constraints of (11) and (12); the number of elevation-azimuth pairs was \(N^{\prime}\). We obtained the sampling set of the elevation and azimuth angles, which are given by Equation (30) and furthermore, the EIRP for the \(n\)th sample in cell \(G_{ij}^{sat}\) was denoted as \(EIRP_{ij}^{A,n}\). Therefore, the probability that \(EIRP_{ij}^{A}\) was less than \(e\) could be represented as Equation (31). \[P(\vartheta\leq\vartheta_{A})=\frac{2\pi R_{e}^{2}(1-cos\vartheta_{A})}{2\pi R _{e}^{2}(1-cos\vartheta_{E})}. \tag{22}\]0  
\[P(\vartheta\leq\vartheta_{A})=\frac{2\pi R_{e}^{2}(1-cos\vartheta_{A})}{2\pi R _{e}^{2}(1-cos\vartheta_{E})}. \tag{22}\]1  
According to the above formulas, we obtained the cumulative probability density function \(EIRP_{ij}^{A}\), which could be converted to a probability density function \(p_{ij}(e)\).
## 3 Materials and Methods
### Proposed Method
#### 3.1.3 Organizing User Purchase Data for Next Sentence Prediction  
Table 2 illustrates how the user purchase data, exemplified in Table 1, are represented as the user's purchase history, emphasizing the presence of concurrent purchases. It also shows the representation of potential next purchase product candidates and the definition of their labels. Specifically, the dataset composition for Next Sentence Prediction (NSP) is processed in the following two steps:  
1. User Purchase History Organization: The order history begins with the opening sentence \"## order history\". Each order is represented as a single sentence, starting with the order completion timestamp, such as \"Order details for 9 May 2023 09:53 \". The order details are then described. If multiple products were ordered together in a concurrent purchase, \"[Concurrent Purchase]' is appended. Following this, the detailed information for each product is listed in the order of \"category_name -- product_name -- attribute_values\". In the case of concurrent purchases, these are connected using the expression ', and '. One thing to note is that while a richer user purchase history is preferable, we included only the order sentences that fit within BERT's maximum context length of 512 tokens.
2. Next Purchase Product Prediction: To enrich the dataset for predictive modeling, we introduced a labeling mechanism aimed at identifying potential future purchases. This system assigns a label of 1 to the user's most recent purchase, reflecting it as a positive example of a likely future choice. Conversely, a label of 0 is assigned to a randomly selected product that the user has not previously purchased, serving as a negative example. This binary labeling is critical for training the model to differentiate between items a user is likely to buy next and those they are less inclined toward.  
By representing user purchase history as text and creating a dataset for predicting the next purchased product, we anticipated that fine-tuning the model to optimize the NSP objective would effectively capture the complex dynamics of customer behavior in e-commerce. We expected that the trained model would better reflect users' interests and recommend products with a higher likelihood of being purchased.
###### Abstract  
The chaos phase modulation sequences consist of complex sequences with a constant envelope, which has recently been used for direct-sequence spread spectrum underwater acoustic communication. It is considered an ideal spreading code for its benefits in terms of large code resource quantity, nice correlation characteristics and high security. However, demodulating this underwater communication signal is a challenging job due to complex underwater environments. This paper addresses this problem as a target classification task and conceives a machine learning-based demodulation scheme. The proposed solution is implemented and optimized on a multi-core center processing unit (CPU) platform, then evaluated with replay simulation datasets. In the experiments, time variation, multi-path effect, propagation loss and random noise were considered as distortions. According to the results, compared to the reference algorithms, our method has greater reliability with better temporal efficiency performance.  
underwater acoustic communication; direct sequence spread spectrum; chaos phase modulation sequence; partial least square regression; machine learning +
Footnote †: journal:  
1
## 2 Related Work on Bridging Between Sensors and Applications
### Frameworks for Internet of Things/Web of Things  
While the Sensor Web describes an infrastructure for heterogeneous sensors, which may be networked or individual, stationary or mobile, and can incorporate _in-situ_ or remote sensing devices,the vision of the two related research fields of Internet of Things [41] and Web of Things [42] is on integrating general, real-world \"things\" with the Internet or Web, respectively. Examples for such things are household appliances, embedded and mobile devices, but also smart sensing devices. Often, the user interaction takes place through a cell phone acting as the mediator within the triangle of human, thing, and Internet/Web. The application fields of the Internet of Things are influenced by the idea of ubiquitous computing [43]. They reach from smart shoes posting your running performance online, over management of logistics (e.g., localization of goods in the production chain), to insurance (e.g., car insurance costs based on the actually driven kilometres).  
For technically realizing the Internet of Things, research topics include protocol stacks for the Internet Protocol (IP) standard optimized for smart things (e.g., IPv6, 6 LoWPAN) [44], naming services for things [45], or the unique identification of objects (e.g., RFID). The Web of Things can be seen as an evolvement of the Internet of Things. It leverages existing Web protocols as a common language for real objects to interact with each other. HTTP is used as an application protocol rather than a transport protocol as it is generally the case in web service infrastructures such as OGC's SWE framework. Things are addressed by URLs and their functionality is accessed through the well-defined HTTP operations (GET, POST, PUT, _etc._). Hence, Web of Things applications follow the REST paradigm [46]. Specific frameworks (e.g., [47,48]) offer REST APIs to enable access to things and their properties as resources. These REST APIs may not only be used to interact with a thing via the Web, also website representations of things may be provided to display dynamically generated visualizations of data gathered by the thing. Then, the mash-up paradigm and tools from the Web 2.0 realm can be applied to easily build new applications. An example application may use Twitter to announce the status of a washing machine or may let a fridge post to an Atom feed to declare which groceries are about to run out.  
However, complex use cases which need detailed and standardized sensor information models and richer functionality on access, discovery, tasking and event handling, such as disaster management or early warning systems, may not be realizable with the Web of Things approach. On the other hand, the integration of smart things into a standardized web services architecture might be too costly and complex in practical applications for simple objects [49]. Approaches such as NASA's Sensor Web 2.0 (Section 2.2) try to combine SWE and Web 2.0 and thereby integrate aspects of the Web of Things with SWE.
## 4 Results and Discussion
### Feature Extraction
#### 4.2.2 Time-Frequency Feature Extraction  
EMD has been briefly introduced in Section 3.1.2. In this section, the differential pressure signals of different flow patterns are decomposed to obtain the IMF components of the signal; the decomposition results are shown in Figure 7.  
As can be seen in Figure 7, most of the IMF3 of slug flow is close to zero, which is obviously different from that of bubbly flow and annular flow. Similarly, the IMF4 of bubbly flow is also very different from that of slug flow and annular flow, not only in forms but also in amplitude. Moreover, for IMF6, IMF7, there are also differences in the amplitudes of modal functions of different flow patterns. Therefore, using Equations (6) and (7), the EMD energy spectra of different signals are defined and calculated, and the corresponding distribution ranges are obtained. Due to the adaptability of the EMD method, the decomposition levels of different signals are different. Considering that the amplitudes of IMF8 and IMF9 of most signals are very low (energy is very low), in order to unify the results, only the energy of level one-level seven is retained. The calculation results are shown in Tables 3-5. It can be seen from Table 3 that the energy proportion of EMD at levels 1-3 is not high, especially from the median point of view; only the bubbly flow in a porous bed with a particle diameter of 6 mm accounts for more than 10% of the energy at level one. It can be seen from Table 4 that the EMD energy of the signal is mainly distributed at levels four, five, and six, especially at level four. As can be seen in Table 5, the difference in EMD energy at level seven between different conditions is large. For example, for the slug flow in the porous bed with particle diameters of 3 mm and 6 mm, the energy proportion may reach more than 40%, while for other conditions, the energy proportion of this level is much lower.  
In general, by processing the signal with the EMD method and calculating the corresponding EMD energy spectrum, new parameters that are different from time domain characteristic parameters can be obtained. These new parameters will show different characteristics with different flow patterns, which can be used for the identification of flow patterns. They also provide richer feature vectors as support for the next machine learning classification.  
Figure 7: IMF components of different flow patterns.