esa-sceva/satcom-chunk-collection huggingface dataset:
# A Comprehensive Study of the Use of LoRa in the Development of Smart Cities
## 4 Results of the Analysis of the Selected Works
### Analysis of the Network and Transport Capabilities  
#### 4.2.1 Data Layer  
Data packets in LoRa can contain up to 255 bytes. This characteristic makes LoRa a low-speed data transfer technology, which is acceptable in sensors networks. Data frames in IoT applications can have different lengths which depend on different factors such as type of data, numbers of packets to be sent, design of the application, and delay requirements. According to our quality assessment, we identified IoT solutions including short packets of 8 bytes or long packets up to 128 bytes. For example, a smart light monitoring solution used 10-byte frames for communication among lights [52], an agriculture applications (monitoring of a star fruit plantation) used 5-byte-long frames [53], and a bike monitoring solution used 12-byte-long frames to determine routes for cyclers [54]. Additionally, a health application used 93-byte data frames to send health information such as systolic/diastolic data and pulse [34]. In the case of water quality, information about salinity and temperature was sent in a 17-byte data frame [55]. The variable data frame length of LoRa provides greater flexibility in the development of IoT applications for smart cities.  
The length of the data frame includes payload and header, and this length can impact on the transmission time and energy consumption. Technical factors that can influence the data length are BW, SF, and throughput. On the other hand, LoRa uses the chirp protocol. The \"up-chirp\" moves upward in frequency, and the \"down chirp\" moves below the central frequency; the central frequency depends of the geographic region and is 915 MHz in the USA (also frequencies in the range between 902 MHz to 928 MHz), 868 MHz in Europe (planned 433 MHz), 470 MHz in China, 923 MHz in Japan, 915 MHz in Australia (also frequencies between 915 MHz to 928 MHz), and 433 MHz in the rest of Asia. LoRa uses three bandwidths: 125 KHz, 250 KHz, and 500 KHz, and SF, that is the duration of the chirp, can be SF7 for the shortest time in air and SF 12 for the longest time in air [56].  
Tables 5 and 6 and Figure 13 show the relation among data rate, frequency, throughput, and payload in LoRa technology. If the selected SF is high, then throughput and payload are low. For example, for the same bandwidth of 125 KHz, in the frequency of 868 MHz, Table 4 indicates that if the selected SF is 7, the throughput is 5470 bps, and the payload is 230 bytes. However, if the selected SF is 12, then the throughput decreases to 250 bps, and the payload is reduced to 59 bytes. Bandwidth selection also affects the length of the payload. For example, in Table 6, for the same frequency of 915 MHz and the same SF of 7, if the bandwidth selected is 500 KHz, the throughput is 21,900 bps, and the payload is 230 bytes; in contrast, if the selected bandwidth is 125 KHz, the throughput decreases to 5470 bps, and the payload increases to 250 bytes.
# Service-Aware Clustering: An Energy-Efficient Model for the Internet-of-Things
## 2 The Energy-Efficient Internet-of-Things Model
### The Service-Aware Clustering Model
#### 2.3.1 The Service-Aware Clustering Problem
By using a probabilistic approach where service and energy awareness are expressed by probabilities of nodes becoming cluster heads or members depending on their service levels and residual energy, the SAC formulation enables striking a good balance between service awareness and energy awareness: e.g., while HSN nodes should naturally be excluded from becoming CH because they are service intensive, there might be cases during a WSN operation where they could still qualify to become the CH when they are more battery energized than CSN nodes.
# An Uncertainty-Driven Proactive Self-Healing Model for
## 4 Discussion  
A significant research subject is the prevention of processing nodes overloading, which leads to performance degradation and the need for task offloading to peer nodes. Some of these offloaded tasks might be in high demand or high priority; thus, efficient mechanisms that allow serving locally more of these tasks should be created. In this paper, we approach this problem by developing a self-healing mechanism that makes a node capable of handling a large volume of tasks without being critically overloaded. This way, we succeed in serving a high percentage of tasks with high priority and demand since we always try to keep space for them in the local queue based on a proactive approach. In the proposed model, we adopt an uncertainty-based reasoning mechanism to manage the uncertainty in the detection of the node as overloaded and the utilization of a monitoring process to secure that there is always enough free space for hosting high-priority and high-demand tasks. We evaluate the performance of our model through various experiments and compare it with a baseline method. Our experimental evaluation demonstrates that the suggested approach can effectively help to achieve the desired outcomes, which are supported by numerical results. Our future research directions involve the definition and adoption of a more complex self-healing methodology for making a decision about when a node is considered as overloaded and when an offload action should take place. Furthermore, an additional future direction is the study of the mobility of users, in combination with the tasks that they render as high demand.  
Figure 10: Average Time for decision offloading of UPSHM for different values of \(\theta\).  
**Author Contributions:** Conceptualization, M.P., P.F. and K.K.; methodology, M.P., P.F. and K.K.; software, P.F.; validation, P.F.; formal analysis, M.P., P.F. and K.K.; investigation, M.P. and P.F.; data curation, P.F.; writing--original draft preparation, M.P., P.F. and K.K.; writing--review and editing, K.K.; visualization, M.P., P.F. and K.K.; supervision, K.K.; project administration, K.K. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Dataset supporting reported results can be found in [[http://iprism.eu/index.php/datasets-ppts/](http://iprism.eu/index.php/datasets-ppts/)]([http://iprism.eu/index.php/datasets-ppts/](http://iprism.eu/index.php/datasets-ppts/)), accessed on 15 July 2022. For more information about the dataset, contact with the corresponding author.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Visual Enhancement Capsule Network for Aspect-Based Multimodal Sentiment Analysis
## 2 Related Work
### Multimodal Sentiment Analysis  
Multimodal sentiment polarity analysis and emotion prediction are two fundamental subtasks of multimodal sentimental analysis. Most of the existing end-to-end methods follow the process of feature encoding-multimodal interaction-emotional fusion-sentimental prediction, in which the emotional interaction between different modalities is the core to making accurate sentiment predictions. [PERSON] et al. used a consistent regression [34] and a syntactic dependency RNN with an attention mechanism [35] for sentiment classification. [PERSON] et al. proposed several network models for multimodal sentiment analysis using image captions, image-guided attention, or iterative features of images and texts [36; 37; 38]. [PERSON] et al. designed the weighted cross-modal attention mechanism for multimodal sentiment analysis, which captured the temporal correlation and the spatial dependence between modalities [39]. [PERSON] et al. proposed a cross-modal complementary network with hierarchical fusion for multimodal sentiment classification [40]. In addition, more deep fusion models were used for modeling intra- and inter-modal semantical interaction [41; 42; 43]. In many multimodal tasks, unified multimodal representations were learned using attention-based adversarial networks [44] and a correlational multimodal variational autoencoder [45].  
Although the current studies have achieved promising results, the aspect-based multimodal sentiment analysis still has some limitations on review data. First of all, compared with text, the emotions expressed by images are more abstract and uncertain, which may be sparse, may have nothing to do with aspects, or may reflect conflicting emotions in some aspects. Second, the learning of deep models relies heavily on large-scale training data, and most multimodal sentiment datasets are labeled by only sentimental polarity [36; 37; 38], which are not suitable for fine-grained aspect-based sentiment prediction. Therefore, in this paper, we use affective entities to guide visual feature learning in reviews, rather than simply utilizing image features for training. At the same time, we build a large-scale MTCom dataset from social networks for aspect-based multimodal sentiment analysis.
## 3 Theoretical Background
### Arnold Map  
The Arnold map is an encryption technique that iteratively scrambles adjacent pixels [24]. If \(I\) is a two-dimensional image with order \(N\), its pixel representation can be written as:  
\[P=\{(x,y)\mid x,y=0,1,2,\ldots,N-1\}\]  
After scrambling, it can be written as:  
\[P^{\prime}=\{(x^{\prime},y^{\prime})\mid x^{\prime},y^{\prime}=0,1,2,\ldots,N-1\}\]The Arnold scrambling transformation can be expressed as follows:  
\[\left[\begin{array}{c}x^{\prime}\\ y^{\prime}\end{array}\right]=\left[\begin{array}{cc}1&1\\ 1&2\end{array}\right]\left[\begin{array}{c}x\\ y\end{array}\right]modN\]  
Here, \(x\) and \(y\) represent the pixel coordinates of the original image, and \(x^{\prime}\) and \(y^{\prime}\) represent the pixel coordinates after performing Arnold scrambling on the original host image. This transformation disperses the host image, ensuring that hidden information remains evenly dispersed even if the original image is corrupted. This criterion enhances the reliability, robustness, and security of the hidden image [24]. In our proposed method, we employ the Arnold map to ensure the security of the watermark image.
## 3 Methodology
### Data Preprocessing
#### 3.2.1 Design of Fault Assessment Indicators  
Faults in PSD systems are usually caused by faults in the door machine system, faults in the control system, or other external factors, such as the intrusion of a foreign body. In order to quantitatively describe the reliability of the platform screen doors in a rail transit station, in combination with the data contained in the original dataset, the fault rate and average fault interval time were selected as indicators to represent the reliability of screen doors.  
The fault rate refers to the probability that a product that has not yet failed at time \(t\) fails within a unit of time after time \(t\), which is recorded as \(\lambda(t)\). For finite samples, the number of samples is \(N\), \(n(t)\) samples fail after time \(t\), and the fault number of the product at time \((t+\Delta t)\) is \(n(t+\Delta t)\); then, the estimated fault rate is  
\[\lambda(t)=\frac{n(t+\Delta t)-n(t)}{N\Delta t} \tag{1}\]  
After a fault in a subway platform screen door, the faulty module is replaced, and the replaced module is returned to the factory for maintenance, which is equivalent to an update of the overall sample after maintenance. In this study, the fault rate in the next cycle (unit time) is approximately equal to the number of faults divided by the total number of samples.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Data Source** & **Data Information** \\ \hline Monitoring Data & Temporal Information & Fault occurrence time \\  & & Fault station \\ \hline \multirow{6}{*}{Maintenance Data} & & Fault content \\  & & Malfunction equipment \\  & Fault Logging & Fault mode \\  & & Fault cause \\  & & Door function status \\ \cline{2-3}  & & Maintenance content \\  & & Response time \\  & Maintenance Record & Handling time \\  & & Processing response time \\  & & Processing delay time \\ \hline \hline \end{tabular}
\end{table}
Table 1: Configuration of the raw dataset for predicting screen door system faults.  
The mean time between faults (MTBF) refers to the average time of normal operation between two faults. It is an important index for reflecting the reliability of equipment. The calculation formula is  
\[MTBF=\frac{Equipment\;operating\;time\times Quantity}{Fault\;times} \tag{2}\]
# A Comparative Analysis of Anomaly Detection Methods in IoT Networks: An Experimental Study
## 5 Discussion  
Anomaly detection in IoT networks is a cornerstone of Industry 4.0 and Industry 5.0, increasing the security, efficiency, and resilience of intelligent industrial systems. Industry 4.0 leverages IoT, cyber-physical systems, and big data analytics to digitally transform production [54; 55]. In this context, IoT networks enable real-time data collection and monitoring, making anomaly detection crucial for identifying deviations from normal system behavior that may signal errors, inefficiencies, or cyberattacks [56; 57].  
The transition to Industry 5.0 introduces a more human-centric approach, prioritizing collaboration between humans and intelligent systems [58]. AI-based anomaly detection methods contribute to this goal by maintaining system security while ensuring seamless human-machine interaction. ML models can detect anomalies in IoT data, facilitating proactive maintenance and minimizing system downtime, which is aligned with the goals of Industry 4.0 and 5.0 [59].  
In both paradigms, the scale and complexity of IoT networks require advanced, scalable anomaly detection techniques. These methods enable automated, continuous monitoring in decentralized systems, a hallmark of smart factories of Industry 4.0 [60; 61]. Furthermore, anomaly detection supports the agility and flexibility required in modern industrial processes by quickly identifying disruptions or inefficiencies [62; 63]. In the case of Industry 5.0, there is a greater emphasis on sustainability and employee well-being, where anomaly detection plays a key role in ensuring workplace safety by monitoring IoT-enabled devices.AI-based detection techniques can be adapted to take into account human factors such as ergonomic hazards, promoting a safer work environment [60; 61; 62; 63].  
Integrating anomaly detection into IoT networks allows industries to mitigate risks, ensure operational continuity, and uphold the fundamental principles of innovation, adaptability, and sustainability. In fact, anomaly detection supports both the optimization of industrial processes in Industry 4.0 and the goals of Industry 5.0, which are human-centric and sustainable [64].  
To address the limitations of traditional, centralized ML approaches, researchers have explored other techniques. The world of ML offers a wide range of opportunities that can significantly impact the quality and scalability of the developed models. By leveraging these advances, we can further improve the effectiveness of anomaly detection in IoT networks, ultimately driving innovation and progress in Industry 4.0 and 5.0 [65; 66; 67].
## 2 Diversity and Distribution of Rice Phytoalexins
### Diterenoid Phytotalexins in Rice
#### 2.1.2. Phytocassanes  
Several phytocassanes were identified in cultivated [29; 30; 31] and wild [9] rice. They share an _ent_-cassane-type diterpene skeleton with a C-11 keto group (Figure 2). Modification of the skeleton by the introduction of keto and hydroxy groups increases their chemical variation. Phytocassanes A-D (Figure 2A-D) were first isolated in 1995 by [PERSON] and co-workers [29] from rice stems infected with _Rhizcotonia solani_ (the causal agent of rice sheath blight) and from rice leaves infected with _Magnaporthe grisea_ (syn.  
M. oryzae, Pyricularia grisea/oryzae, responsible for rice blast disease). Phytocassanes E and F (Figure 2E,F) were extracted, respectively, from suspension-cultured rice cells elicited with mycelial extracts of the potato pathogenic fungus _Phytopththora infestans_[30] and UV-irradiated rice leaves [31].  
It has been shown that phytocassanes, as well as momilactones, accumulate not only in the aerial parts but also in the roots of rice plants. [PERSON] et al. [50] reported that the roots of rice seedlings biosynthesiseiry photocassanes A-E and monilactones A and B, which are largely released into the environment through exudation. Unlike momilactones, phytocassanes did not show allebotathic activity against dicot seedling growth. Therefore, the authors hypothesized that these compounds might play a role in defense against soil pathogens such as _M. grisea_. Indeed, it has been reported that this fungal pathogen can invade rice roots using a typical root-specific pathway [51].  
As mentioned above, the production of diterpenoid phytooleoxins in cultivated and wild rice was recently investigated by [PERSON] and co-workers [9]. All examined cultivars, except for 'Jinguoyin', contained phytocassanes A and D, which were also detected in most wild species, especially those with the AA genome (within the genus _Oryzaa_, species with AA and BB genomes are phylogenetically close to each other and distant from FF [52]). Furthermore, they isolated two undescribed phytooleoxins, namely oxyalactone (an isomer of momilactone A) (Figure 1F) and phytocassane G (a di-dehydrogenated phytocassane A) (Figure 2G), from the cultivars 'Basilamon' and 'Jaguary', respectively. All cultivars in the WRC (except for 'Jinguoyin' and 'Phulba') showed the phytocassane G-accumulating chemotype, while only three cultivars showed the oxyalactone-accumulating chemotype.
## 4 The Impacts of Microalgae/Cyanobacteria and Their Extracts on Crops' Production
### Direct Growth Stimulation
Treatment of a paddy field with _Anabaena_ isolates resulted in an overall increase in seeds' germination rate and in an improvement of plant properties, such as plant height, root length and fresh and dry weights of leaves, stems and roots [127]. In addition, an improvement of soil properties, namely moisture and porosity, was observed. In the study performed by [PERSON], et al. [130], aqueous extracts of _Gracilaria corticata_ and _Enteromorpha flexuosa_ were applied to maize and sunflower seeds. Although extracts from both species resulted in increased root and shoot lengths and dry weights, these effects were more pronounced in plants treated with _E. flexuosa_ extracts. After evaluating the composition of both extracts, the authors attributed the most promising effects of _E. flexuosa_ extracts to the higher contents of phytohormones (indole-3-acetic acid, gibberellins and cytokins) determined for this organism. [PERSON], et al. [39] evaluated the effect of applying a solution of total polysaccharides extracts obtained from _S. platensis_ in tomato and pepper cultures. With this study, the authors demonstrated that application of the extracts' solution resulted in an increase in the size of both tomato and pepper plants. In addition, increased root weight, size and number of nodes was registered for the treated plants.
# Emulating Cued Recall of Abstract Concepts via Regulated Activation Networks
## 3 RAN Methodology to Simulate Recall Operations
### Geometric Back-Propagation Operation  
Geometric back propagation (GBP) is a downward propagation mechanism in RAN modeling. This method enables us to determine an activation vector \(a_{m}\) (\(<a_{1},..,a_{i},..,a_{m}>\)) at layer \(L\)-1, for an expected activation (E-A) vector \(A^{\prime}_{n}\) (\(<A^{\prime}_{1},..,A^{\prime}_{j},..,A^{\prime}_{n}>\)) at layer \(L\). This operation is a window operation that takes place between two adjacent layers, i.e., layers \(L\) and \(L\)-1. For instance, if the RAN model has three layers, L\({}_{0}\) (input layer), L\({}_{1}\), and L\({}_{2}\) (output layer), then two GBP operations take place, first between L\({}_{2}\) and L\({}_{1}\), and then between L\({}_{1}\) and L\({}_{0}\). Figure 7 shows the single-window operation between two layers.  
Figure 6: Excitatory, inhibitory, and neutral effect of CSRL weights (W) when transformed using the impact factor \(\sigma\).  
The GBP mechanism commences with an expected activation (\(A_{n}^{\prime}\)) vector at layer \(L\). Next, a starting input vector \(a_{m}\) (\(<a_{1},..,a_{i},..,a_{m}>\)) is injected in layer \(L\)-\(1\). Now, we enter into a cycle where we propagate the activation of the nodes in layer \(L\)-\(1\) upwards to layer \(L\) and determine the observed activation (O-A) vector \(A_{n}\) (\(<A_{1},..,A_{j},..,A_{n}>\)) at layer \(L\). Furthermore, an error vector \(e\) is calculated using \(A_{n}^{\prime}\) and \(A_{n}\) (expected and observed activation vector) through Equation (10). The error vector \(e\) is used to determine an accumulated delta value \(\triangle_{a_{i}}\) (see Equation (12)) based upon the function expressed by Equation (11). This \(\triangle_{a_{i}}\) value is then added to the activation vector at layer \(L\)-\(1\) (see Equation (13)) to obtain a new input vector \(a_{m}^{new}\). Additionally, the cycle is repeated with the new \(a_{m}^{new}\) input at layer \(L\)-\(1\) until the error is minimized or the cycle equals the user-defined maximum iteration threshold. Algorithm 2 presents the detailed geometric back-propagation algorithm. As mentioned earlier, the GBP operation takes place between two consecutive layers. However, if the hierarchy has more than two layers, then with the window operation, it is possible to propagate down the injected E-As at the nodes of the top-most layer \(L\), to input layer \(0\).  
\[e_{j}=A_{j}^{\prime}-A_{j} \tag{10}\]  
\[\triangle_{a_{i},A_{j}}=(W_{j,i}-a_{i})*(e_{j}) \tag{11}\]  
\[\triangle_{a_{i}}=\sum_{j=1,..,i}\triangle_{a_{i},A_{j}} \tag{12}\]  
\[a_{i}^{new}=a_{i}+\triangle_{a_{i}} \tag{13}\]
## 2 Methods
### Detection Algorithms  
#### 2.4.1 Solidity Detection and Object Size Filtering  
This detection algorithm is depicted in Figure 2. As the shape of the MS lesions is often an ellipsoid or a circle, the algorithm is based on the curvature level (solidity) of each object in the image. The algorithm operates as following:  
1. The images are converted into two binary images with different thresholds. This is done in order to collect all the relevant objects by simplifying the identification process.
2. Each threshold image goes through two additional levels of filtration: 1. Filtering objects by size: objects that are either too small or too large are discarded. 2. Filtering out all objects with curvature that does not match MS lesions statistics.
3. The two resulting filtered images are combined into one: candidate MS lesions are positive in both images.
4. The two combined images (each with different initial threshold) are combined into one, resulting in a full picture, with enhancement of lesion-suspected objects and reduction of the irrelevant objects.