esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Optimization of DSPE Conditions
#### 3.2.3 Effect of the Desorption Solvent, Mode, and Time  
Acetonitrile and methanol, as the most common eluents, have good compatibility with different pesticides. In our study, acetonitrile (ACN), methanol (MeOH), ACN with 0.5%  
Figure 3: Effect of the amount of zeolite H-Beta (**A**), adsorption time (**B**), desorption solvent (**C**), desorption mode and time (**D**) on neonicotinoids (a: vortex 10 min and ultrasonication 10 min; b: vortex 10 min; c: ultrasonication 10 min; d: vortex 5 min and ultrasonication 5 min; e: vortex 1 min and ultrasonication 1 min).  
(\(v/v\)) FA, and ACN with 0.5% (\(v/v\)) ammonium hydroxide (NH4 OH) were examined for the desorption efficiency of the eight neonicotinoids from the zeolite H-Beta sorbent. The results indicated that 2 mL of ACN exhibited a better elution efficiency than 2 mL MeOH did, which may be due to its greater polarity. Meanwhile, the introduction of NH4 OH significantly improved the recoveries of acetamiprid and thiazolprid. It can be seen from Figure 3C that ACN with 0.5% NH4 OH (2 mL) could desorb all targets with acceptable recoveries. It was therefore chosen as the eluent for the subsequent works. We examined different combinations of vortex and ultrasonication time. As shown in Figure 3D, the recoveries of the eight neonicotinoids did not change dramatically with different desorption modes and time. Therefore, to reduce the time for sample preparation, 1 min of vortexing and 1 min of ultrasonication time were applied in the following procedure.
# Reduction of Water Color in a Spinning Disc Reactor
## 4 Discussion
### Performance of SD Reactor in Water Color Reduction in the Selected Working Regime  
For the RR-dye-based colored water treated with ZnAILDH in a spinning disc setup, the water flow rate, within the mentioned limit values, could influence to some extent the color removal. However, the disc rotational speed, especially in the low to medium range, seemed to have a stronger influence. It is difficult to explain these types of effects, however the SD efficiency lay within the strong micromixing characteristics, which was enhanced especially at higher disc rotational speeds, and a sufficiently large residence time of the water on the disc was such that the intense mixing benefits manifested in the process occurring on the disc such as water discoloration due to adsorption in connection with the SD technology [7; 9]. Furthermore, the size of the tested adsorbent particles influenced the adsorption characteristics and, thus, the color removal efficiency.  
Additionally, in comparison to a common batch system using ZnAILDH as an adsorbent, the SD technology based on the same ZnAILDH rendered a significant improvement in terms of water discoloration.  
Based on Table 4's data obtained for the TiO\({}_{2}\)-treated colored water, one can conclude that the highest investigated spinning disc speed of 800 rpm combined with an average water flow rate of 6.00 L/h rendered the highest color removal value of 67.55%. In addition, the low flow rates associated with medium spinning disc speeds, similar to the conditions used in case of ZnAILDH, provided good color removal results, namely 52.85% at 6.00 L/h and 250 rpm operation conditions.  
A comparison between the color removal data obtained in the SD setup, when using ZnAILDH and TiO\({}_{2}\), indicated better discoloration values for the latter material for all the investigated colored water flow rates and disc rotational speeds.  
Al\({}_{2}\)O\({}_{3}\) as an adsorptive material did not display good adsorbent properties within the SD configuration.  
Furthermore, the use of the Fenton oxidation process in the spinning disc system supplied with colored water and ZnAILDH provided an even better color removal than in the case of the simple SD with the same tested adsorptive material treatment. For the treatment with TiO\({}_{2}\), FO applied in the SD system provided a higher discoloration value of 35.92% after only a 20 min working time period, which was higher than the 13.95% color removal obtained in the simple SD system. After further system operation up to 65 min, the color removals obtained in the simple SD and the FO in SD systems seemed to be close enough.
# A Short-Term Forecast Model of foF2 Based on Elman Neural Network
## 2 Model Principle  
### Elman Neural Network (ENN)  
The Elman neural network was first proposed by [PERSON] in 1990 for speech processing. Based on the neural network, the ENN adds the context layer to connect the input and output of the hidden layer, which enable the network to store previous state and have dynamic properties. Compared with BP neural networks, ENN is better able to capture information about sequence data. The change of foF2 is stable in a short time, and the correlation of foF2 in adjacent time is great. Thus, we chose the Elman neural network to build the model. The Elman neural network structure is shown in Figure 1:  
The Elman neural network consists of four layers, which are input layer, hidden layer, context layer and output layer [11]. The input layer transmits external signals to the hidden layer. The context layer stores the output signal of the hidden layer and serves as the input for the next step of the hidden layer. Then, the hidden layer activates the signal it receives and passes it to the next layer. Finally, the signal is output by the output layer.  
Suppose \(m\) is the length of input, the input set is:  
\[\mathbf{X}=[x_{1},x_{2},\cdots x_{m}] \tag{1}\]  
\(n\) is the total number of hidden layers and \(t\) is the input set number. The output of the hidden layer at \(t\) step is:  
\[\mathbf{H}_{i}(t)=\left\{\begin{array}{cc}f(\mathbf{W}_{i}\mathbf{X}(t)+ \mathbf{W}_{ii}\mathbf{Z}_{i}(t))&i=1\\ f(\mathbf{W}_{i}\mathbf{H}_{i-1}(t)+\mathbf{W}_{ii}\mathbf{Z}_{i}(t))&1<i\leq n \end{array}\right. \tag{2}\]  
where \(\mathbf{H}_{i}(t)\) represents the output of the hidden layer \(i\), \(\mathbf{W}_{i}\) represents the weight matrix from the hidden layer (\(i\)-1) to the hidden layer \(i\), \(\mathbf{Z}_{i}(t)\) represents the output of the context layer \(i\), which is given by:  
\[\mathbf{Z}_{1}(t)=\left\{\begin{array}{cc}\mathbf{H}_{1}(t-1)&t>1\\ 0&t=1\end{array}\right. \tag{3}\]  
\(f\) represents the activation function of the hidden layer, which can be calculated from the following:  
\[f(x)=\frac{1}{1+e^{-x}} \tag{4}\]  
Figure 1: Multi-hidden layer Elman neural network.  
and \(y(t)\), which represents the output of the network, is given by:  
\[y(t)=\mathbf{W}_{n+1}\mathbf{H}_{n}(t) \tag{5}\]  
where \(\mathbf{W}_{n+1}\) represents the weight matrix from the hidden layer n to the output layer.
## 4 Fractal Analysis to Support Oral Cancer and OPMDs Diagnosis and Prognosis
### Limitations of the Studies  
Not all applications of fractal analysis to support OC and OPMD diagnosis were successful. In some cases, the results did not reach statistical significance; in others, they failed.  
As an example, the studies on actinic cheilitis did not find the fractal analysis to be effective in distinguishing healthy lips from solar cheilosis-affected lips [34]. However, it must be considered that clinical pictures instead of histological samples were used for fractal analysis in these cases.  
In fact, a key point first highlighted by [PERSON] et al. [39] was that \"the irregularity is not fully reproduced at all scales\". They noticed that the same nuclear borders appeared more irregular at lower magnification and smoother at higher magnification.  
Hence, the fractal object and methodology must be accurately chosen, from the acquisition mode, to the image magnification and processing, up to the fractal analysis methods.  
Furthermore, it is not always true that \"more is better\": in some cases, combining fractal analysis with other texture measures did not reveal significant advantages over fractal analysis alone in discerning subtypes and grades of similar histopathological diseases [30] or among different OPMDs such as oral lichen planus and leukoplakia [45].
# On the Rate-Distortion Theory for Task-Specific Semantic Communication
## 5 Experimental Results
### Results for Generation Tasks
#### 5.2.3 Impact of Side Information  
In order to observe the role of side information in the proposed semantic communication systems, we also conduct experiments under different settings of side information. Figure 5 demonstrates the simulation semantic rate-distortion curves under different settings of the dimensions of feature vectors of side information, on both MNIST and CIFAR10 datasets. Both the results in Figure 5a and those in Figure 5b show that the required rate can be reduced when side information at the encoder and the decoder is accessible, and the reduced rate increases with the the amount of information in \(Y\), as measured by the value of \(d_{Y}\). The above observations are aligned with the theoretical results of the Gaussian semantic rate-distortion functions in Theorems 1 and 2. This again justifies the fact that offering side information as the background knowledge of the users can effectively save communication resources.  
Moreover, we observe that the reduced rate due to the existence of side information is much higher at low rates than at high rates, demonstrating that side information plays a more crucial role when channel capacity is limited. For the MNIST dataset, at the lowest rate of 2 bits, the MSE distortion decreases significantly from 0.133 (no side information) to 0.091 (\(d_{Y}=2\), \(-31.6\%\)) and further to 0.075 (\(d_{Y}=4\), \(-43.6\%\)). In contrast, at the highest rate of 12 bits, the distortion only reduces from 0.0075 to 0.0068 (\(-9.3\%\)) and 0.0061 (\(-18.7\%\)) for \(d_{Y}=2\) and \(d_{Y}=4\), respectively. A similar trend is observed for CIFAR10. At 64 bits, the distortion drops substantially from 0.173 to 0.113 (\(-34.7\%\)) and 0.082 (\(-52.6\%\)) when side information is introduced, while at 384 bits (\(12\times 32\)) the improvement is more modest,  
Figure 4: Experimental semantic rate-distortion functions for generation task under different \(L_{o}\) values. (**a**) Results on MNIST; (**b**) Results on CIFAR10.  
Figure 5: Experimental semantic rate-distortion functions for generation task under different values of \(d_{Y}\). (**a**) Results on MNIST; (**b**) Results on CIFAR10.  
from 0.018 to 0.015 (\(-\)16.7%) and 0.013 (\(-\)27.8%). These results clearly indicate that side information provides the greatest relative benefit in low-rate regimes where the channel capacity is severely constrained. The additional background knowledge helps compensate for the lack of transmission bandwidth, enabling more efficient semantic communication. As the rate increases, the marginal utility of side information decreases, as the system can rely more on the primary channel to convey necessary information. This observation aligns with theoretical expectations and reveals the importance of incorporating side information, particularly in bandwidth-limited scenarios.
# Occlusion-Robust Multi-Target Tracking and Segmentation Framework with Mask Enhancement
## 2 Related Works
### Instance Segmentation-Based Multi-Object Tracking  
With the development of object detection and instance segmentation, the accuracy of MOT algorithms has significantly improved. [PERSON] et al. introduced the MOTS task based on MOT datasets [21]. MOTS integrates detection, segmentation, and tracking to achieve pixel-level multi-object tracking.  
Mask R-CNN [13] became a standard framework: it performed well on datasets like COCO [22], Cityscapes [23], and MVD [24]. It featured three branches for regression, classification, and segmentation. The core principle of the architectures involves adding a mask prediction branch to existing Fast R-CNN [25] or Faster R-CNN [26] frameworks, and the mask prediction branch utilizes an encoder-decoder Fully Convolutional Network (FCN) [27] structure.  
Video instance segmentation (VIS) methods include propagation-based approaches [13; 28; 29; 30; 31; 32] that refine masks using temporal information. In this field, [PERSON] et al. [30] integrated a pedestrian re-identification module into a video instance segmentation system. [PERSON] et al. [31] improved mask propagation through clustering. For crowded scenes, [PERSON] et al.  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Method** & **Technical Features** & **Advantages** \\ \hline Online methods [2; 3; 4; 5; 6; 7; 8] & Sequential frame processing & Real-time performance \\ \hline Filter-based [9; 10] & Motion/appearance modeling & Fast computation \\ \hline MHT [5; 14] & Multi-Hypothesis Trees & Comprehensive association \\ \hline [PERSON] et al. [15] & CNN features with MHT & Improved appearance modeling \\ \hline [PERSON] et al. [16] & LSTM-enhanced MHT & Long-term dependency capture \\ \hline [PERSON] et al. [17] & Spatial–temporal attention & Occlusion handling \\ \hline [PERSON] et al. [18] & LSTM networks & Temporal dependency modeling \\ \hline [PERSON] et al. [8] & Deep residual networks & Frame-to-frame similarity \\ \hline [PERSON] et al. [19] & CenterNet & Background bias reduction \\ \hline \hline \end{tabular}
\end{table}
Table 1: Summary of trajectory association algorithms.  
[4] used spatial-temporal trees, while others focused on occlusion detection: [PERSON] et al. [33] compared target features, [PERSON] et al. [34] maintained feature pools, and [PERSON] et al. [35] scored occlusion patterns.
## 2 Related Studies
### Other Related Studies  
DTN that uses portable storage units was proposed in [6]. Using clusters to obtain information about victims who require rescue in an area where telecommunication services are disrupted was proposed in order to improve the efficiency of searching for these victims in [7]. An attempt to improve both delivery speed and delivery rate by calculating possible connection duration time and choosing to connect to those adjacent nodes with which connections can be established over a long period was proposed in [8]. A method of using DTN to enable a rescue team to share information in a disaster area was proposed in [9]. Specifically, the paper proposed a method in which a rescue team member who has a mobile terminal collects data on the disaster situation, and uploads the data to a server installed in a disaster response center quickly and reliably taking the degree of importance of each item of the collected data into consideration. A routing method that suppresses flooding of messages in DTN was proposed in [10]. Flooding messages is suppressed by defining a \"super node\", and using a routing that is based on route information. Message ferries that interconnect clusters and carry messages were defined in [11; 12; 13]. Similarly, using DTN as a sensor network that incorporates traveling nodes was proposed in [14; 15; 16; 17].  
However, all these studies use a routing based on either _Epidemic Routing_ or _Spray and Wait_. Therefore, they retain the problem of precious battery charge being consumed, which makes it difficult  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**MethodItem** & **Delivery rate** & **Storage usage** & \multicolumn{2}{c}{**Likelihood of network congestion**} & **Battery consumption** \\ \hline _Epidemic Routing_ & High & Large & High & Large \\ _Spray and Wait_ & Low to high & Small to large & Low to high & Large \\ \hline \hline \end{tabular}
\end{table}
Table 1: Characteristics of the existing methods.  
to use mobile terminals of victims to collect information about the safety of victims in the aftermath of a disaster. In addition, they do not propose any specific bundle layer protocol.
## 3 A Short Overview of Selected Examples of Measuring and Monitoring Systems on Sea Ships
### Measurement of the Operational Parameters of Main Engine
In such a situation, presented in Figure 6c, the periods of electrical signal are different and they are proportional to the measured value of torque M according to the formula:  
\[\mathrm{M}=\frac{\mathrm{k_{T}(t_{1}-t_{2})}}{\mathrm{n}} \tag{1}\]  
where k\({}_{\mathrm{T}}\)--coefficient depends on construction of shaft and teethed wheels, n--revolution, t\({}_{1}\), t\({}_{2}\)--time of pulses received from photo-optical detector. Figure 5: Main engine (ME) layout with actual operation points for one-month ship operation provided by SeaPerformer system; the records below the main engine layout present momentary changes of RPM on the ME shaft in the above-defined period (Courtesy of Enamor Ltd.). The method was developed [17; 18] and patented in GMU [19] as well as implemented on many ships in cooperation with ENAMOR Ltd. company. Contribution of GMU in state-of-the-art development consisted of elaboration of a new solution of torque meter related transmitter [19]. A novelty of the transmitter solution [19] is based on special construction including two mutually advantageous elements, placed on the rotating shaft and each of them consist of the spacer pipe (distance sleeve) and measuring ring with evenly and radially distributed at the circumference adequate teeth. An advantage of this transmitter construction is use of only one photo-optical detector, that limits the influence of changeable conditions and work parameters of transmitter and electronic sets of photo-optical detector circuit on measurement accuracy. The validation tests of the propulsion control assistance system ETNP-10 type have executed by Research and Development Company ENAMOR Ltd. [14]. The investigated system installed on propeller shaft is presented in Figure 7[14]. Figure 7a shows two teeth rings with photo-optical detector that are installed on propeller. The local human-machine interface presents current measurement parameters (Figure 7b) or engine load based on engine layout (Figure 7c). Figure 6: Photo-optical method for torque measurement, (**a**) concept of torque measurement, (**b**) relation between teeth of two rings and electrical signal for torsion angle \(\varphi=0\), (**c**) relation between teeth of two rings and electrical signal for torsion angle \(\varphi\
eq 0\). Figure 7: Propulsion control assistance system ETNP-10, where position of photo-optical detector is marked by ellipse, (**a**) two teeth rings installed on propeller shaft with photo-optical detector, (**b**) example of measurements, (**c**) actual engine load based on engine layout (Courtesy of Enamor Ltd.).
### Related Works  
Throughput optimization in wireless networks has been studied by many researchers. [PERSON] [25] considered optimization of sensing-and-transmit energy in cognitive radio using a Markov decision process to improve throughput. However, this technique is computationally expensive, compared to most conventional spectrum sensing techniques. [PERSON] et al. presented a study of a sensing-throughput tradeoff for cognitive radio networks in an energy detection scheme [26]. They modified the link layer and proposed selective cooperative diversity with an automatic repeat request to increase throughput.  
To improve the performance of spectrum sensing, various researchers have proposed different techniques. A survey of spectrum sensing techniques based on signal processing was done by [PERSON] and [PERSON] [14]. Recently, machine learning algorithms have drawn attention for spectrum sensing, [PERSON] et al. [17] proved that an SVM-based classifier with a firefly algorithm outperforms a naive Bayesian classifier, a decision tree, a support vector machine, linear regression, and the hidden Markovmodel when applied to spectrum sensing. Simulation results obtained by [PERSON] and [PERSON] [27] presented a study of multi-class SVM-based cooperative spectrum sensing decision making. The SVM was implemented in _one-versus-rest_ and _one-versus-one_ approaches in order to compare the results. It was concluded that increasing the number of samples per sensing slot improved the accuracy of the classifier. [PERSON] et al. [28] compared the performances of unsupervised and supervised learning techniques in cooperative spectrum sensing. In the family of supervised learning techniques, the SVM-based and the weighted k-nearest neighbor-based classifiers were recommended due to high receiver operating characteristic performance and, in some applications, low training and classification time. [PERSON] and [PERSON] [29] presented a performance study comparing energy detector-based and SVM-based spectrum sensing considering a conventional two-class hypothesis, i.e., PU presence and absence. [PERSON] et al. [30] utilized a multi-layer perceptron network with backpropagation to improve throughput performance of a secondary user in a cognitive radio network. The signal-to-noise ratio (SNR) of the secondary link was 20 dB. The performance of the network was satisfactory due to such an elevated level for the SNR. However, the performance of systems with low SNR levels was not presented.  
The work in this paper is an extension of the machine learning-based algorithms for spectrum sensing. However, the proposed system considers quantization of the energy range under hypothesis \(H_{0}\) of the received signal via spectrum sensing and quantizing the residual energy of a node. The transmission power of an SU node is selected based on the received signal energy strength and the residual energy in the node. The results support the statement that the proposed scheme improves the throughput of the SU channel for a limited battery capacity of SU node.