esa-sceva/satcom-chunk-collection huggingface dataset:
# Succulent Plant Image Classification Based on Lightweight GoogLeNet with CBAM Attention Mechanism
## 2 Materials and Methods  
### 2.1. Succulent Data Collection  
The dataset for this study comprises a total of 691 succulent plant images covering 10 categories. The samples were obtained from a succulent planting site in cooperation with Southwest Forestry University. The image data were captured by a Huawei Mate 30 cell phone (40 megapixels, f/1.8 wide-angle lens) at an angle of 90\({}^{\circ}\) and a height of 50 cm. Image data processing included two steps: first, the succulent images were cropped using OpenCV's Sobel edge detection method to ensure that the samples had a uniform image background; second, data enhancement was performed, including image flipping, resizing (224 \(\times\) 224), and grayscale conversion to improve the model's generalization ability. Meanwhile, to ensure effective training and reasonable evaluation of the model, we divided the dataset into a training set and test set in a ratio of 9:1, where the training set images were used for the training of the model and parameter optimization, while the test set images were used for evaluating the generalization performance and classification accuracy of the model. Table 1 details the specific distribution of the 10 succulent plant categories and their corresponding image samples.  
In Table 1, we find that some categories have a high degree of similarity, which is likely to lead to overfitting of the model. Specifically, Crassula perforata and Graptoveria 'Opalina' have similar leaf colors and surface textures, while Haworthia cooperi var. truncata and Haworthia truncata also have strong morphological similarities. In addition, Sedum burrito and Sedum rubrotinctum further increase the risk of model confusion due to their spherical leaves and dense arrangement. To alleviate the overfitting problem caused by these category similarities, we have taken two optimization measures in the model training process: first, we have expanded the original image dataset by applying various data augmentation strategies. These data augmentation strategies have effectively improved the robustness of the model to image transformations. Second, we introduced the CBAM attention mechanism into the model structure. By fusing channel attention and spatial attention modules, the model is guided to focus on more discriminative feature regions in the image, thereby further improving the model's ability to distinguish between similar categories. To visually demonstrate the confusion that may occur when there are visually similar categories in the model, we have adjusted the structure of Table 1. The three columns at the end of Table 1 specifically compare examples of images from categories that are prone to confusion.
### Related Work
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Reference** & **Description** & **Remarks** \\ \hline [PERSON] et al. [42], 2015 & The timeline of LEACH and its descendants are compared based on selected features such as scalability and mobility & Compares considering a few parameters \\ \hline [PERSON] et al. [43], 2014 & Performance comparisons between LEACH protocol variants are made & Less number of examples discussed \\ \hline [PERSON] et al. [44], 2013 & Highlights advantages and disadvantages of various routing protocols inspired by LEACH where CH selection plays an important role. & A brief comparison in terms of the CH selection process, load balancing, routing, and security \\ \hline [PERSON] et al. [45], 2013 & Performance measurements of energy-efficient protocols conducted & Improvement regarding application-specific deployment of nodes needs to be conducted \\ \hline [PERSON] [46], 2012 & A comprehensive survey of clustering routing protocols is conducted & Gives an idea about possible future research directions \\ \hline [PERSON] [47], 2012 & Comparative study of CH selection algorithm & Scalability and stability factors are not explored \\ \hline \hline \end{tabular}
\end{table} 
Table 1: _Cont._  
Figure 3: Issues of clustering protocols. Hence, to overcome these problems, several algorithms have been proposed. As far as possible, this article intends to present a more comprehensive survey that discusses a wide range of routing protocols that will help researchers gain knowledge about routing protocols to work in proper future directions. The classification of routing protocols is performed based on a homogenous and heterogeneous environment suitable for the specific application. A homogeneous WSN consists of nodes with the same initial energy, and a heterogeneous sensor network contains nodes of two or more energy levels. The type of network can be chosen depending on the requirement of the application.
## 3 Aggregated Fraction Model and Generalized Model: Comparison
### Solution Quality  
However, the generalized model provides more flexible solutions that allow to get much better solutions in terms of the objective function value. Denote an optimal solution objective function value for instance \(I\) of the generalized model and aggregated fraction model, respectively, as \(V_{g}(I)\) and \(V_{af}(I)\), and \(X(I)\) as the ratio of these values,  
\[X(I)=V_{g}(I)/V_{af}(I).\]  
In Table 4 there is summary data of \(X(I)\) for two datasets of instances. On average, in both cases the solution provided by generalized model has two times less objective function value.
# A Deep Belief Network Classification Approach for Automatic Diacritization of Arabic Text
## 6 Experiments and Results
### Children Stories: A Novel Corpus of Arabic Diacritized Text  
Corpora are built for a wide range of applications such as modeling language use for linguistics research, material for education, or training data for NLP applications. Research in NLP for Arabic language is drawing attention as it is becoming one of the most common languages used on the internet. Today, there are more than 168 million Arabic internet users worldwide and more than 2.1 billion Arabic indexed Web pages [88] and the availability of resources and tools is being developed [89].  
The availability of a diacritized Arabic corpus has been a challenge for researchers in order to progress in new directions to solve the problem of automatic Arabic text diacritization. The matter of diacritized text is crucial for new learners to read Arabic and to the applications of text-to-speech systems.  
There is only a small number of benchmark corpora for this purpose that limit the scope of dealing with it. In this paper, we present a corpus called children's stories. The corpus is freely available, it contains 26 thousands MSA words collected from children's stories books. The size of this corpus may be considered moderate or in some cases small compared to the size of the benchmarks used for Arabic NLP applications, however, it is unique as it has a different linguistic structure due to the type of books it is collected from and the audience these books were written to. Thus, more features may be extracted using machine learning algorithms. This resource can be leveraged for educational purposes and for researchers to use in developing NLP applications.  
Our DBN model was tested using the children's stories dataset and the performance of the model was evaluated in terms of DER, WER, DER-1 and WER-1. Table 6 illustrates the results of this evaluation. As observed from the results, we get an improvement of 44% DER and 57% WER when ignoring the diacritic mark of the last character. Clearly, we have an improvement that we cannot underrate and is directly related to the type of  
Figure 14: Probability distributions of class labels for the example word from the children’s stories corpus.  
text we are dealing with and the linguistic structure it has. For example, most of the text written in children books and stories has a lot of rhymes, and the words and sentences are ordinarily shorter.
## 4 Discussion
### Industry 4.0 & Key Performance Indicators  
The set of questions related to Industry 4.0 and KPIs are: \"What new Industry 4.0 digital technologies are relevant to assembly operations?\", \"How to measure the improvement?\" and \"How to make the most out of them?\"  
There are many examples of new technologies applied to final assembly--see Table 3: the Internet of Things, big data and digital twins for improving processes and decisions as well as for gathering data and obtaining valuable information; Cyber Physical Systems and Augmented/Virtual Reality for measuring human operator performance and supporting human operators' work; and a mix of technologies to support different aspects that enable mass customization. However, assembly operations are likely to experiment an evolution rather than a revolution by gradually incorporating these technologies. Two key areas will be of particular interest: enhancing the role of people in assembly operations--especially in terms of responsibility and skills; and making possible human-machine hybrid systems capable of efficient low-volume high-variability production.  
To evaluate the performance of assembly systems, a KPI system is employed. Standard KPIs need to be adapted in order to include both traditional metrics (e.g., cost, quality, throughput, inventory, lead time, productivity) and new indicators that are specific to the products, operations, stakeholders, business goals and IT-related aspects of the smart factory.  
Despite the wealth in the literature about what new technology can do, few relate to methodologies to assess the operational needs and opportunities in final assembly and then finding or developing an Industry 4.0 solution to them.
## 7 The Evaluation of BOC-Like Receiving of MBOC
### Performance Evaluation
#### 7.2.4 Multipath Resisting Performance  
In this subsection, a comparison of the multipath resistance ability of different MBOCs are provided, under unmatched BOC\({}_{11}\)-like receiving, which may help understand the instinct performance of different MBOC implementations. Figure 9 shows the average range error envelope Equation (22) of QMBOC, TMBOC, CBOC+ and CBOC\(-\) at receiving bandwidth 40 MHz and 10 MHz, where MDR is \(-5\) dB.  
As can be seen from the Figure 9a, when receiving bandwidth is 40 MHz, among the unmatched receiving of the four MBOCs, the multipath resistance ability is ranked as CBOC+ > QMBOC > TMBOC > CBOC\(-\). Also, the matched receiving of QMBOC achieves better anti-multipath performance than the unmatched received MBOCs. When receiving bandwidth is 10 MHz, however, it shows no obvious difference between the four MBOCs under unmatched receiving. Matched receiving also shows the best anti-multipath performance at 10 MHz bandwidth.  
Finally, it should be noted that in the previous signal evaluations by utilizing equivalent Gabor bandwidth and anti-interference rate for code tracking, we decoupled the evaluation criteria from external factors, such as the SNR, interference strength, receiver implementation details, etc. While the factors including mismatch of the despreading waveform are treated as an essential factor. This was done because the local despreading waveform is always selected according to the characteristics of the multi-components multiplexed signal. To reduce the receiving complexity, it is common to process a proportion of the components. For instance, the unmatched BOC\({}_{11}\)-like receiving of MBOC is achieved by discarding the BOC(6,1) term in the construction of the local replica. The unmatched BPSK-like receiving of high-order BOC or ACE-BOC is essentially achieved by ignoring the step-shaped subcarrier of the received signal in the local replica generation. Otherwise, if an unmatched receiving method is arbitrarily configured without the consideration of the structure of signal, it may deteriorate the received SNR seriously, thus affecting the operating of receiver. Therefore, the diverse unmatched receiving approach for the signal does not depart from the scope of the evaluation of the internal factors of signal.
# An IoT-Based Solution for Intelligent Farming +
## 2 Related Work
### IoT/M2M Protocols and Communication Technologies
#### 2.1.2 Network Layer  
Regarding the Network Layer, the Internet Protocol (IP) is the predominant choice, with two coexisting versions, IPv4 and IPv6. The latter one was born due to the exponential increase of the number of addressable devices and consequent exhaustion of available IP addresses. Consequently, the introduction of IPv6 was inevitable, being introduced gradually on all networking devices. Nevertheless, and despite its popularity and interoperability, its direct use on IoT devices is not always reasonable. The overhead of such protocols (and inherent processing needs), usually clashes with the constraints associated to IoT devices. Thus, a big effort was made by the 6 LoWPAN working group (WG) from IETF, in order to minimize the IPv6 limitations and to make it suitable to be used by IoT devices. As a result of such work, the 6 LoWPAN protocol was released [11], defining the specifications of the Network Layer of IP-based WPANs based on IEEE 802.15.4 physical and MAC layers. This protocol defines an intermediate layer between the MAC and Network layers, the Adaptation Layer (AL), that aims at ensuring the interoperability between the referred layers, implementing compression, fragmentation and reassembly mechanisms. Despite the advances attained by this AL, the complexity introduced for supporting IPv6 is still too high for technologies that excel by their simplicity, low power consumption and low overhead. Some examples are the Bluetooth Low Energy (BLE) and Near Field Communication (NFC). Hence, the 6 lo working group extended the 6 LoWPAN protocol to these two technologies, establishing the IPv6-over-foo AL [33]. Pursuing  
Figure 2: Popular IoT standards on an Internet Protocol (IP)-based solution (based on [7]).  
similar goals, [PERSON] et al. [33] proposed a similar solution for integrating RFID systems into IPv6 networks. Notwithstanding, and despite all the efforts, there will always exist a significant overhead associated to the implementation of such ALs, which may make them inappropriate for tightly constrained applications.  
Still within the Network Layer, the Routing Protocol for Low Power and Lossy Networks (RPL) [34] is the main option considered when deploying routing on 6 LoWPAN solutions. This protocol was specified by the IETF Routing Over Low Power and Lossy Networks WG (ROLL) and it briefly consists on a distance vector routing protocol that uses Destination Oriented Directed Acyclic Graphs (DODAG) to define routes. The construction of the graph is made through an objective function (OF) that dynamically defines the routing metrics to be used taking into account the network constraints. In addition to the support of different traffic flows, as point-to-point, point-to-multipoint and multipoint-to-point, RPL adapts itself to the network rate and accepts routing metrics such as link quality or current battery status of devices, in exchange for a higher computational cost.
## 3 Results and Discussion
The so-called p-type channel or substrate is interpreted as boron dopants-in the silicon lowering the Fermi energy. Once the applied Gate bias bents the intrinsic Fermi energy down below the Fermi energy, the region becomes strongly inversed to n-type semi-conductor making the channel conductive. The thickness of the conductive layer may as well be calculated by solving [PERSON]'s Equations as stated in Equations (4) and (5) with W set to 60 nm. The channel concentration is then estimated to be \(p\) = 3.66 \(\times\) 10\({}^{23}\) (m\({}^{-3}\)), which is substituted into (K\({}_{\rm B}\)T) ln(\(p/n_{i}\)) to obtain 0.438 eV (energy difference between E\({}_{\rm F(i)}\) and E\({}_{\rm F}\)). Therefore, the strong inversion layer is determined to be 203 angstroms, which was one half of the parabolic curve (in Equation (4)) in Figure 6, and is surprisingly about one third of the whole fin width. \begin{table}
\begin{tabular}{c c c c} \hline
**Gate Bias** & **W120L240\_fit** & **w120L160\_fit** & **W120L100\_fit** \\ \hline V\({}_{\rm G}\) = 1.00 V & 1.09000 \(\times\) 10\({}^{-4}\) & 1.25000 \(\times\) 10\({}^{-4}\) & 1.43000 \(\times\) 10\({}^{-4}\) \\ V\({}_{\rm G}\) = 0.75 V & 1.27000 \(\times\) 10\({}^{-4}\) & 1.30000 \(\times\) 10\({}^{-4}\) & 1.4000 Figure 5: \(k_{N}\) is supposed to be proportional to the channel width or the inverse of channel length. (**a**) \(k_{N}\) versus \(1/\mathrm{L}\) with fin width = 120 nm, (**b**) \(k_{N}\) versus \(1/\mathrm{L}\) with fin width = 115 nm, and (**c**) \(k_{N}\) versus W with channel length = 160 nm. \[\Rightarrow p=\frac{2 eV_{G}}{eW_{1/2}} \tag{5}\]  
where  
\[ \begin{array}{l}K_{B}=1.38\times 10^{23}\;\mathrm{J/K},\\ e=1.69\times 10^{-19}\;\mathrm{Coul},\\ n_{i}=1.45\times 10^{16}/\mathrm{m}^{3},\\ T=298\;\mathrm{K},\\ \varepsilon=11.9\times 8.85\times 10^{-12}\;\mathrm{F/m}\end{array} \]
## 1 Introduction
[13]: a 3D computational model for a shell-and-tube LHTS was calibrated with experimental results. This was successively reduced to a one-dimensional radial model by employing an effective diffusivity technique. According to the authors, this model is suited to predict performance over long-time spans. An original simple model was proposed by [PERSON] et al. [14]. This is based on the effectiveness-number of transfer units, and it was calibrated on a tube-in-tank salt-based PCM system with radial circular fins. The correlation proposed by the authors is semi-empirical, and it can be used for sizing and optimizing LHTS systems. A further interesting simplification methodology was suggested by [PERSON] et al. [15]. First, they analyzed the heat transfer properties of a tube assembly cross-section through a 2D finite volume model. Then, they produced a 1D radial model in Dymola for the ensemble of PCM and fins able to yield comparable results. Finally, they coupled this latter model with a 1D axial model for the HTF again in Dymola and studied the time evolution of the thermal heat flux for different configurations. Nevertheless, although a few studies investigated the integration of LHTS in existing heating systems, little attention has been devoted to the analysis of LHTSs operating conditions and partial charge/discharge. For instance, [PERSON] et al. [16] analyzed the behavior of an LHTS unit in a District Heating (DH) substation through a 2D finite volume model. [PERSON] et al. [17] studied the performance of an encapsulated LHTS unit in a residential heating system in Sweden by means of a finite element model. [PERSON] et al. [15] designed an LHTS for the production of high-temperature steam in a cogeneration plant using the simplified model previously mentioned. However, in order to facilitate LHTS market development, their optimal integration in energy systems is essential. Dynamic models that are fast and accurate could positively support the study of LHTSs at a system level. In this work, a compact 0D model for a modular shell-and-tube LHTS is proposed. The model is able to predict the thermal power released or absorbed by the assembly as a function of its state of charge. Furthermore, it can represent partial charge and discharge operations. Therefore, this model can be easily deployed for simulations at a system level in order to investigate optimal control strategies or demand side management (DSM). Indeed, the proposed model is here applied to two relevant case studies regarding the integration of LHTS in DH and multi-energy systems.
## 5 Results
### Results for Spherical Images  
We now shift the presentation of results to the spherical image scenario, where participating subjects were viewing images in an immersive fashion. The MAE results are illustrated in Figure 6.  
We initially note that the application of all electrodes and channels results in a fairly low overall error, about 0.1, with noticeable differences between subjects. Corroborating our earlier observations in the AR scenario, we additionally note a slightly higher level of error and variability for the QoE case compared to the QoS case. Comparing the results to those presented for the regular image scenario, we notice an increase in the prediction error. Specifically, we observe that this baseline for prediction in the spherical case performs approximately as the two-electrode scenario for regular images. A decrease of the available EEG sensor data to sensor pairs immediately catapults the error to about over 0.5 on average with significant variability amongst subjects. A comparison with the regular image scenario results yields again a level of error that is more comparable to the single electrode scenario for those. Relying on the feedback of single electrodes increases the overall error to almost one, paired with high inter-subject variability. In turn, any predictions could result in significant differences from the true underlying values and render systematic exploitation of EEG signals difficult.  
The corresponding \(R^{2}\) scores attained from the model application are illustrated in Figure 7.  
Figure 6: Mean Absolute Errors (MAE) for the spherical image (SAR) condition averages and standard deviations for subject ratings (QoE) and impairment level (QoS) prediction performance analysis of individual subjects.  
Figure 7: \(R^{2}\) scores for the spherical image (SAR) condition averages and standard deviations for subject ratings (QoE) and impairment level (QoS) prediction performance analysis of individual subjects.  
Similar to prior results, we attain the best model performance for the inclusion of all four electrodes in the modeling effort. While not as high as for the regular image scenario, the \(R^{2}\) scores are fairly high and can be considered excellent for prediction efforts. Attempts at system complexity reduction yield almost a halving of the coefficient of determination accompanied by increased variability. Further reduction to single electrode inputs yields an even worse outcome, whereby negative \(R^{2}\) scores are attained for the overall average. Given the nature of the \(R^{2}\) score, this refers to the model not only being unable to capture the underlying variability and explain it, but the modeling approaches for single electrodes introduce even more undesired outcome variability.
## 7 Security Analysis
### Informal Security Analysis
#### 7.1.4 Session key disclosure attack  
In the proposed scheme, \(U_{ma}\) cannot calculate \(SK_{ij}=h(R_{SD}||R_{UC})\) because \(U_{ma}\) cannot compute authentication request message \(\{M_{1},M_{2},M_{3}\}\) without knowing random nonce \(R_{SD}\) and secret parameter \(X_{i}\). Therefore, our protocol can withstand session key disclosure attack.
# An Efficient Scalable Scheduling MAC Protocol for Underwater Sensor Networks +
## 4 Problem Definition
### Hidden Terminal Problem  
The phenomenon of a hidden terminal occurs when a sensor node cannot sense one or more nodes that can overlap with its transmission. Figure 2 illustrates this situation in which sensors B and C are hidden from each other. Hence, a concurrent transmission by sensors B and C results in a collision in sensor A. The existence of a hidden terminal leads to increasing the number of collisions and retransmissions, and thus results in higher energy consumption and lower network throughput.