esa-sceva/satcom-chunk-collection huggingface dataset:
# Detecting Suicidal Ideations in Online Forums with Textual and Psycholinguistic Features
## 5 Discussion  
### Classification  
#### 5.1.1 Experiment 1 (Individual Features)  
From the first experiment, we observed that models trained with TF-IDF and NRC features did not perform well, since their average F1 scores were lower than 50%. The suboptimal performance of TF-IDF-trained models could potentially be attributed to the phenomenon described as the curse of dimensionality. A high number of TF-IDF features makes the data sparse and subsequently impedes the model's ability to generalize. In the training dataset consisting of 628 examples, there are, on average, 5902 TF-IDF features. A possible explanation for the low F1 score among models trained with NRC features is the fact that NRC features correspond to only 11 psychological constructs. This is likely not sufficient for the model to effectively capture the differences between classes. Another type of psycholinguistic feature we tested was LIWC. The use of LIWC features generally resulted in better classification performance arguably, because it utilizes a more extensive set of dictionaries that correspond to a wider range of psychological processes and linguistic dimensions. This finding supports the effectiveness of LIWC at capturing underlying linguistic signals related to distress, emotional traits, and cognitive processes that indicate suicidal ideations. Consequently, it highlights the importance of incorporating psychologically grounded tools into the development of suicidal ideation detection models.
# Water Quality Monitoring: A Water Quality Dataset from an On-Site Study in Macao
## 2 System Design
### Hardware Components
#### 2.1.3 Turbidity Sensor  
The turbidity sensor measures the cloudiness or clarity of water by emitting a light beam through the liquid [19] and measuring the intensity of light scattered or absorbed by suspended particles, with the output voltage corresponding to turbidity levels in NTUs (nephelometric turbidity units), crucial for detecting contamination caused by particles like silt, algae, and organic matter. The measurement error of the turbidity sensor is typically \(\pm 2\) NTU or \(5\%\) of the reading, whichever is greater. Accuracy is influenced by factors such as light source stability, sensor contamination, and sample consistency.  
The following cubic polynomial describes the relationship between NTU and the sensor output voltage in Figure 6.  
The formula for converting voltage to NTU is given as  
\[y=-1120.4x^{2}+5742.3x-4352.9 \tag{3}\]  
Figure 5: TDS sensor voltage vs. TDS concentration (** denotes superscript).  
Figure 6: Turbidity sensor voltage vs. turbidity concentration.
# Anti-Recompression Video Watermarking Algorithm Based on H.264/AVC
## 4 Experimental Results and Analysis
### Comparison Experiments with Similar Algorithms
#### 4.6.2 Comparison of Recompression and Requantification  
Figure 9 shows the results of the gravimetric comparison experiment with the literature [24]. In the literature [24], the initial QP in the gravimetric experiment is 20, and the value range of QP in the second compression is [16; 23]. It can be seen from Figure 9 that the BER value of this algorithm is lower than that of the literature [24] and has better robustness.  
Table 6 shows the results of comparative experiments with the literature [25]. In the literature [25], the initial QP of the anti re-compression experiment is 28, the initial QP of the requantification experiment is 24, and the QP of the second compression is 22. From Table 6, it can be seen that the algorithm has better performance of anti-compression and quantification than the literature [25].  
\begin{table}
\begin{tabular}{c c c} \hline
**Experiment Video** & **SSIM of Ref. [24]** & **SSIM of Ours** \\ \hline Mobile & 0.9951 & 0.9991 \\ Stefan & 0.9845 & 0.9968 \\ Foreman & 0.9941 & 0.9992 \\ Bus & 0.9901 & 0.9993 \\ Hall & 0.9763 & 0.9986 \\ \hline \end{tabular}
\end{table}
Table 4: The invisibility comparison between the algorithm in this paper and the algorithm in [24].  
\begin{table}
\begin{tabular}{c c c} \hline
**Experiment Video** & **SSIM of Ref. [25]** & **SSIM of Ours** \\ \hline Mobile & 0.988 & 0.9967 \\ News & 0.970 & 0.997 \\ Tempete & 0.988 & 0.9962 \\ Flower & 0.972 & 0.9994 \\ Waterfall & 0.981 & 0.9979 \\ \hline \end{tabular}
\end{table}
Table 5: The invisibility comparison between the algorithm in this paper and the algorithm in [25].  
Figure 9: Comparison of BER values between the algorithm in this paper and the algorithm in the literature [24] under the recompression attack.
## 4 MFG-LEACH Protocol  
The routing mechanism of the MFG-LEACH protocol is an enhancement of the traditional LEACH protocol. The primary objective is to balance energy consumption among sensor nodes by considering the collective state of the network. Similar to traditional LEACH, MFG-LEACH operates in rounds and begins with the setup phase, where clusters are formed. Each sensor node independently decides whether to become a CH based on a probabilistic approach that considers a set threshold. Once a CH is selected, the steady-state phase begins where data transmission occurs. Unlike traditional LEACH, which uses a static approach to compute \(E_{k}\) according to (2), in MFG-LEACH, to derive \(E_{k}\), we utilize a coupled system of two equations: the HJB and FPK equations. The FPK equation evolves forward in time and dictates the evolution of the mean field. Conversely, the HJB equation evolves backward in time and governs the computation of the optimal control policy for node \(k\). The continuous feedback loop is key to ensuring that the system dynamically adapts to changes, such as varying node densities, energy levels, or network conditions. As these environmental factors change, the mean field shifts, prompting agents to modify their strategies dynamically.
# Development and Application of a Novel Non-Iterative Balancing Method for Hydronic Systems
## Appendix B Analysis of Cases Where the Farthest Terminal Is Not the Most Disadvantaged
### Manifold-Based and Reverse Return Cases  
In case of manifold-based water systems (Figure 11), this issue is easily faced since the presence of the manifold allows the concept of farthest user to be overcome; in fact, if the manifold is properly sized it offers negligible pressure drops and all user branches in parallel are supplied at the same pressure.  
In such cases, the procedure can be adapted by starting not from the farthest but directly from the most disadvantaged MD terminal. A suitable solution is also the installation of pressure taps directly on the manifold and using the differential pressure between supply and return manifold as the reference.  
If this is not practical, and it is preferred to take one terminal as reference, it is required to identify the MD terminal: the techniques for MD terminal detection are reported in the following.  
The reverse return configuration presents quite an analogy with the manifold-based case; the working pressure of each user is invariant because of the connection as the different allocation of line drop losses between supply and return are composed in such a way as to be self-compensating. This makes these two types of circuitry (manifold-based and reverse return), conceptually assimilable each other and can be approached in the same way, simply by identifying the MD terminal and starting from that. The assessment proposed for the detection of the MD path can also be applied to the case of reverse return systems, with some small (and described) adaptation.
## 6 Implementation of The Six-Bit DSA  
Figure 12 shows the complete schematic of the 6-bit DSA, which has an attenuation range of up to 31.5 dB in 0.5 dB steps. The 0.5 dB and 1 dB attenuation bits use a simplified T-type structure to minimize insertion loss; the 2 dB and 4 dB attenuation bits use a modified T-type structure based on SICS; the 8 dB attenuation bit uses a modified \(\pi\)-type structure based on SICS; and the 16 dB attenuation bits use an innovative multi-type structure based on SBCS to minimize amplitude errors and phase fluctuations. Since the parasitic capacitance associated with the transistors and the interconnection structures presents a negative imaginary impedance, this shifts S\({}_{11}\) and S\({}_{22}\) down along the 50 \(\Omega\) circle on the Smith chart. Inserting series inductors \(Lm\) between bits improves the matching characteristics, thereby maximizing operational bandwidth. In this paper, transmission lines are used instead of inductors. In addition, the transistors in each attenuation bit are optimized to achieve a balance between insertion loss and phase error.
## 3 The Model Description and Data Preprocessing
### RNN Models  
#### 3.1.1 Long Short-Term Memory (LSTM)  
As is shown in Figure 1, a layer of typical LSTM is defined by Equations (1)-(6) as follows:  
\[z_{t}=\tanh(W_{z}x_{t}+U_{z}h_{t-1}+b_{z}) \tag{1}\]  
\[i_{t}=\text{sigmoid}(W_{in}x_{t}+U_{in}h_{t-1}+b_{in}) \tag{2}\]  
\[o_{t}=\text{sigmoid}(W_{out}x_{t}+U_{out}h_{t-1}+b_{out}) \tag{3}\]  
\[f_{t}=\text{sigmoid}\Big{(}W_{for}x_{t}+U_{for}h_{t-1}+b_{for}\Big{)} \tag{4}\]  
\[c_{t}=i_{t}\circ z_{t}+f_{t}\circ c_{t-1} \tag{5}\]  
\[h_{t}=\tanh(c_{t})\circ o_{t} \tag{6}\]  
where \(z_{t}\) is an input to be added to cell state, \(c_{t}\) is an internal cell state, \(h_{t}\) is a hidden state at next time. Additionally, \(i_{t},\ o_{t},\ f_{t}\) represent outputs from input gate, output gate, and forget gate, respectively. From here, unless otherwise specified, the operator symbol \(\circ\) representsthe Hadamard product, \(W,\ U\in\mathbb{R}^{m\times n}\) are weighting matrices, \(b\in\mathbb{R}^{n}\) is bias, regardless of the subscript, and the explanations are omitted hereafter. In addition, \(x_{t}\) is an input at time step \(t\), \(h_{t-1}\) is a hidden state at time step \(t-1\), and \(h_{t}\) is a hidden state at time step \(t\) in this paper.  
The input gate controls input to cell state, using the value of \(i_{t}\ (0<i_{t}<1)\) which is the output from Equation (2) so that it can be selectively stored from the input. Additionally, the output gate controls the output from cell state using the value of \(o_{t}\ (0<o_{t}<1)\) which is the output from Equation (3). Finally, the forget gate controls the internal cell state directly by \(f_{t}\ (0<f_{t}<1)\) which is the output from Equation (4) [2; 3].
# Hash Based DNA Computing Algorithm for Image Encryption
## 3 Methods
### Design of DNA Hash Encoding Module  
The DNA hash encoding module is designed to transform image data into a DNA-encoded form with different DNA bases, which could involve significant properties of the hash function, i.e., fixed-length output, small computing burden, tampering resistance and anti-collision ability. Such property help improve the unbreakability of the proposed method with low computing cost. As shown in Table 1, there only exist eight encoding patterns that satisfy the \"complementary\" rule, where each two bits satisfys the condition that there XOR results should be true. For example, if 00 and 01 are encoded as A and C, respectively, 11 and 10 should be encoded as T and G, respectively. There are only eight encoding rules obtained through this combination, and they are all listed in Table 1. Based on the analysis of the \"comple  
Figure 1: The overall structure of the proposed method, including sender and receiver. They both contain a DNA hash encoding module and a DNA decoding module. In fact, content-aware decryption module can be regarded as the inverse version of content-aware encryption module. mentary\" rule, we convert image data into a bit stream with the size of 3 bits to describe 8 different mapping rules, where the 2 bit is mapped as one DNA base for the encoding basis. Meanwhile, two more 8-bit streams are designed at the beginning of the stream to record the length and width of the original image, which could help restore the input image after encryption in a proper way. Essentially, the proposed DNA hash encoding module plays a crucial role in encryption, which randomly employs various sets of complementary rules to encode each pixel. We demonstrate the overall encoding process of it during encryption in Figure 2. The detailed steps of the encoding and decoding process inside the DNA hash encoding module are also listed with pseudo codes in Algorithms 1 and 2. First, we adopt the SHA256 algorithm to generate two important parameters, i.e., initial key value and initializing factor. To effectively improve the security of the cryptosystem, we then feed the computing factor \(p\) into the PWLCM, generating the rule selection sequence with the following equation:  
\[x_{n+1}= \begin{cases}\dfrac{x_{n}}{p},&0\leq x<p\\ \dfrac{x_{n}-p}{0.5-p},&p<x\leq 0.5\\ F(1-x_{n},p),&0.5<x\leq 1\end{cases}  \tag{1}\]  
It's noted that \(p\in(0,0.5]\), which enables variety in outcome with little modification in key values, whi ch is highly resistant to attack. Finally, we use the rule selection sequence to help encode the bitstream, which serves as DNA rules to encode pixels with DNA-based rules as shown in Table 1.  
Figure 2: Encoding process inside DNA hash encoding module.
# Genetic Algorithms to Maximize the Relevant Mutual Information in Communication Receivers +
## 5 Conclusions  
In this article, genetic algorithms were successfully applied for the optimization of parametrized compression mappings that shall preserve a maximum possible amount of relevant information. These mappings were used to build subsystems of communication receivers, i.e., channel output quantizers and demodulators. To the best of our knowledge, our conference version of this article [23] described the first application of genetic algorithms for the maximization of mutual information in this context. It investigated potential applications of this principle for distance-based channel output quantization. The results were also included in this article. The resulting distance-based quantizers can compete with quantizers designed with the KL-means information bottleneck algorithm while requiring significantly fewer parameters for their description. The graph-based approximate nearest neighbor search algorithm used in this application allows for a tunable complexity and only needs a small number of distance calculations.  
As a novelty, we have developed the idea of maximizing the relevant mutual information in communication receivers with genetic algorithms further and also presented entirely new results. We have introduced the idea to apply either approximate or exact nearest neighbor search in \(K\)-dimensional trees in the receiver-sided signal processing to build signal processing blocks that aim for maximum preservation of relevant information. That technique was exemplarily used to build a novel demodulation technique for a data transmission scheme using non-binary low-density parity-check codes. The resulting demodulators can achieve the performance of a non-quantized optimum reference scheme up to a small fraction of a decibel over \(E_{b}/N_{0}\), even though all costly signal processing breaks down to a simple and very efficient search in a \(K\)-dimensional tree. We have also shown that using an approximate nearest neighbor search instead of an exact one does not cause significant performance degradation, but further reduces the complexity of the considered mappings based on \(K\)-dimensional trees.  
The proposed method is very generic and can also be applied to other signal processing problems. A possible future application of the proposed method could be the reduced complexity decoding of non-binary low-density parity-check codes.  
Conceptualization, J.L., S.J.D., R.M.L.; methodology, J.L., S.J.D., R.M.L.; software, J.L., S.J.D., R.M.L.; validation, J.L., S.J.D., R.M.L.; formal analysis, J.L., S.J.D., R.M.L., M.A., M.S., P.J.; investigation, J.L., S.J.D., R.M.L., M.A., M.S., P.J.; resources, M.A.; writing--original draft preparation, J.L.; writing--review and editing, J.L., S.J.D., R.M.L., M.A., M.S., P.J.; visualization, J.L., S.J.D., R.M.L.; supervision, M.A., P.J.; project administration, M.A., M.S., P.J. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The authors declare no conflict of interest.
## 5 Conclusions  
Aiming to grapple with the low fault location accuracy caused by the complex short-circuit fault characteristics in active distribution networks, this paper comprehensively considers the measurement data of intelligent terminals in the feeder and distribution power supply area, and proposes an algorithm for a fault section location in active distribution networks based on the solution of the virtual fault state value. The following conclusions are drawn:  
1. This paper analyzes the observability of faults in the distribution network. Intelligent terminals are installed at key nodes and low-voltage side nodes of transformers to measure the voltage drop value before and after the fault occurs. Additionally, the algorithm performs time synchronization processing on the measured data to provide data support for fault section location.
2. The linear least squares method is used to quickly solve the virtual fault state equation, which has high accuracy for various short-circuit fault types and fault impedances.
3. The proposed algorithm directly solves the voltage value of DG as the fault state quantity, which can remove the uncertainty of the power flow direction caused by the grid connection of DG, and has high applicability in the active distribution network with DG.
4. According to the application of the proposed method in IEEE-37 nodes and IEEE-8500 nodes, this method can complete the fast and accurate location of faults. At the same time, the algorithm is also very accurate in identifying fault types. In the case of high distributed power access, the accuracy of this algorithm will not be significantly affected.
5. The fault location method based on this algorithm also occasionally makes location errors. The general positioning failure is related to the special situation of the line model, such as the line length being too short and the node having no load. However, the wrong location is still close to the actual fault line.  
In general, through the verification of distribution network cases of different scales, it is proven that the method proposed in this paper eliminates the influence of a high proportion of DGs, and can accurately determine the location and type of faults. Compared with other algorithms, it is also verified that this algorithm has higher discrimination accuracy.  
Conceptualization, G.R. and B.J.; methodology, B.J.; software, X.H.; validation, B.J.; formal analysis, G.R.; investigation, G.R.; resources, X.Z.; data curation, X.Z.; writing--original draft preparation, G.R.; writing--review and editing, J.X.; visualization, K.T.; supervision, X.Z.; project administration, X.Z.; funding acquisition, G.R. All authors have read and agreed to the published version of the manuscript.  
This research is funded by the Science and Technology Project of State Grid Jiangsu Electric Power Company, grant number J2021215.  
Not applicable.  
The authors would like to thank the editors and anonymous reviewers for improving this paper.  
The authors declare no conflict of interest.
## 5 Experiments and Analysis
### Student Learning Analysis Lising Regression
#### 5.4.1 Lasso Regression  
For Lasso regression, the selected features are \"code_module\", \"imd_band\", and \"total_click\". The feature coefficients are 3.43, 0.31, and 9.95, respectively. The findings indicate that the GB model exhibits superior performance compared to the other models, as evidenced by its lower MAE and RMSE values of 18.92 and 24.29, respectively. The RFR exhibits strong performance, as evidenced by an MAE value of 19.10 and an RMSE value of 23.95. The SVR and LR models exhibit higher values of MAE and RMSE compared to the other models, which suggests a relatively inferior level of predictive accuracy. Figure 4 depicts the results of the machine learning models' outcome achieved by employing the features extracted using the Lasso regression technique.  
Figure 4: ML models’ outcome using Lasso feature selection.
# Design and Analysis of Systematic Batched Network Codes
## 2 Ordinary Batched Network Coding
### Decoding Algorithms
#### 2.3.3 Inactivation Decoding  
When \(K\) is relatively small or the coding overhead is small, BP decoding tends to stop before decoding all the message packets. Although we can continue decoding by Gaussian elimination, the computational complexity is high.  
A better approach is to use _inactivation decoding_: when BP decoding stops, an undecoded message packet is marked as inactive and substituted into the batches as a decoded packet to resume the BP decoding procedure. The decoding of batches with inactive packets also induces linear constraints on the inactive packets. Eventually, all the message packets are either decoded or inactive. The inactive packets are then solved by the linear constraints induced by decoding batches and the precodes. Inactivation decoding has the same decoding performance as Gaussian elimination, but can have a much lower computation cost if the number of inactive packets is small.  
Moreover, when using inactivation decoding, we can use the inactive packets during encoding. Inactive packets during encoding are treated as inactive from the beginning of inactivation decoding and hence are also called _pre-inactive packets_. The extra inactive packets added during decoding are called the _dynamic inactive packets_. See [23] for a detailed discussion of inactivation decoding for BATS codes.  
Denote by DEC the decoder that implements one of the above decoding processes of the BATS outer code. The pseudocodes of DEC for two-step decoding are given in Appendix C for reference.
# Internet of Drones: Routing Algorithms, Techniques and Challenges
## 5 Future Research Directions
### Packet Delivery Ratio  
After sufficient and best routing is available, the packet delivery ratio (PDR) determines the amount of data that can efficiently be transferred during a given amount of time. When the data loss is less and network structure is reliable, the PDR is ensured [63]. In congested networks, however, packets can drop, leading to decrease in PDR. The authors in [64] have proposed a procedure to improve PDR by selecting those CHs that have energy remaining and can determine the node locations. Data loss can be problematic in WSNs because they are not able to detect and gather important information, for example, fire detection and disaster management. The military usage is also affected by data loss and node failure, which is why the clustering technique is used to mitigate the data loss effects. As opposed to a large data transfer at a given time, clustering can merge smaller data ratios and reduce the PDR, and packets can be sent/received much more efficiently [1].