esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results

Table 2: Statistical analysis of survey questions according to the current use of cigarettes. \({}^{*}\) shows statistical significance (\(p<0.05\)). \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \multicolumn{4}{c}{**Gender**} \\ \cline{2-5}  & \multicolumn{2}{c}{**Male**} & \multicolumn{2}{c}{**Female**} & \(p\)**-Value** \\ \hline  & **Yes** & **No** & **Yes** & **No** & \\ \hline Currently smoking & 46.7\% & 53.3\% & 32.7\% & 67.3\% & 0.028 \({}^{*}\) \\ \hline Used tobacco in the past & 59.7\% & 40.3\% & 43.8\% & 56.3\% & 0.015 \({}^{*}\) \\ \hline Smokeless tobacco & 0.7\% & 99.3\% & 0 & 100\% & 1.00 \\ \hline Tried to stop using tobacco & 26.3\% & 73.7\% & 17.0\% & 83.0\% & 0.092 \\ \hline Tobacco may cause mouth cancer & 87.5\% & 12.5\% & 87.6\% & 12.4\% & 1.00 \\ \hline Alcohol use & 54.7\% & 45.3\% & 31.9\% & 68.1\% & 0.001 \({}^{*}\) \\ \hline Alcohol may cause mouth cancer & 65.4\% & 34.6\% & 66.4\% & 33.6\% & 0.894 \\ \hline Difficulty chewing/swallowing as a symptom of oral cancer & 38.0\% & 62.0\% & 42.5\% & 57.5\% & 0.517 \\ \hline A mouth sore that does not heal as a symptom of oral cancer & 66.4\% & 33.6\% & 61.1\% & 38.9\% & 0.428 \\ \hline Abnormal mass/lump in the mouth as a symptom of oral cancer & 59.1\% & 40.9\% & 59.3\% & 40.7\% & 1.00 \\ \hline White/red patch in the mouth as a symptom of oral cancer & 35.8\% & 64.2\% & 48.7\% & 51.3\% & 0.053 \\ \hline The slow change in voice quality as a symptom of oral cancer & 29.9\% & 70.1\% & 39.8\% & 60.2\% & 0.110 \\ \hline Quit tobacco use to prevent oral cancer & 75.2\% & 24.8\% & 85.9\% & 15.0\% & 0.060 \\ \hline Quit alcohol use to prevent oral cancer & 65.7\% & 34.3\% & 67.3\% & 32.7\% & 0.893 \\ \hline Avoid contact with secondhand smoke to prevent oral cancer & 34.3\% & 65.7\% & 41.6\% & 58.4\% & 0.241 \\ \hline Brush and floss your teeth twice/day to prevent oral cancer to prevent oral cancer & 51.1\% & 48.9\% & 50.4\% & 49.6\% & 1.00 \\ \hline Telling your dentist when your dentures do not fit well to prevent oral cancer & 23.4\% & 76.6\% & 22.1\% & 77.9\% & 0.880 \\ \hline Education about oral cancer & 40.1\% & 59.9\% & 33.9\% & 66.1\% & 0.357 \\ \hline Education about human papilloma virus and oral cancer & 42.3\% & 57.7\% & 29.2\% & 70.8\% & 0.035 \({}^{*}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of survey questions according to gender. \({}^{*}\) shows statistical significance (\(p<0.05\)).
## 4 Application Example, Results and Discussion
### Software  
To support a user in all tasks related to data processing and evaluation, graphic user interface (GUI) software, depicted in Figure 3, was developed. To display and navigate through the data, the GUI implements a tree based navigation with filtering functionality that is always visible on the left side of the software. To sort and structure the data, the measurements are hierarchically arranged top-down starting with the analytes, followed by the virtual sensors which group the associated measurements for the specific combination together.  
The software is divided into tabs, according to the introduced algorithms: View + Manual Validation, Auto Validation and Sensor Ranking. Each tab encapsulates the controls and  
Figure 2: Graphs of boosting function \(p_{j}\) for different \(\phi_{j}\).  
views needed for the respective use case. Depending on the active tab, the navigation is either used to browse through all measurements, choose a reference curve for the similarity algorithms or select the combinations of sensors and analytes for the ranking.  
The filter enables the user to specify the following parameters:  
* Analyte
* Sensor Substrate
* Sinter Temperature
* Sinter Time
* Sensor Operating Temperature
* Validation Status  
Furthermore, the user can add measurements to a list of favorites or use the reference checkbox to obtain a list of all measurements that have been used as references in the curve similarity algorithm.  
Visual inspection is realized with four interactive graph views divided into two subgroups. The upper graphs are used to display all measurements for the selected combination of virtual sensor and analyte, whereas the lower ones show the specific measurement selected in the navigation tree. The user is able to inspect the data by applying several filters and standardizations (e.g., first derivative, baseline normalized resistance, etc.).  
After an in-depth inspection, a validity status can be assigned to the measurement manually by the user. A measurement can have three validation states:  
* _Valid_
* _Invalid_
* _Not Validated_  
All measurements are initially in the _Not Validated_ state. Manual validation and annotation is realized with four numbered radio buttons, a commentary field and two buttons. Remarks and textual annotations can be added to the Comment text field. In addition to the mentioned states, _Reevaluate_ marks the measurement for later inspection, whereas _Skip/Reset_ either resets its validation state back to _Not Validated_ or skips the measurement if it is _Not Validated_. Care was taken to minimize the amount of clicks by adding keyboard shortcuts and effective tabbing. Using the shortcuts, the validation and textual comments are saved and the software automatically navigates to the next curve for inspection without any needed mouse interaction.  
Figure 3: The software showing the View + Manual Validation tab.
# A Framework to Understand Attitudes towards Immigration through Twitter
## 3 Dataset  
In this section, we describe the Twitter platform and the dataset used to measure attitudes towards immigration.  
Twitter is a micro-blogging platform where users report a screen name, a full name (which can be real or fictitious), a location (real, fictitious, or empty), and a small autobiography, among other attributes. Each user publishes posts (called _tweets_) with a maximum of 280 characters. Twitter also allows interaction between its users: users can _follow_ others users and to see their tweets in their own timelines. Users can mention other users in their own tweets using a handle (e.g., @username), _quote_ other tweets or adding commentary to them, or _retweet_ (RT) another tweet to share it with one's audience.  
To crawl tweets that discuss immigration in Chile, we connected to the Twitter Streaming API using a system designed to crawl Chilean tweets [68]. The query parameters were keywords related to immigration, e.g., \"immigration\" (_immigration_), \"immigrante\" (_immigrant_), \"fronteras\" (_borders_), \"racismo\" (_racism_), etc.; and origin countries with their respective denomyms. Thus, the dataset after data cleaning is composed by 160,775 tweets (54,252 are plain tweets and 106,532 are retweets--RTs-. In addition, 20,248 tweets are quotes and 19,265 are replies) that are on topic during 2017, written by 36,698 users (see Figure 2 for the temporal distribution of content).  
The cleaning process ensured that the discussion under analysis was about human migration in Chile. Examples of noise topics were: racism toward indigenous groups in Chile, bird migration, a South American soccer championship, national presidential elections, migration issues in Mexico, the U.S.A., and Spain, among others. Hence, we excluded tweets from users with a reported (or predicted) location different to Chile, as well as tweets in languages other than Spanish.  
Figure 1: Number of immigrants in Chile by year of arrival according to the 2017 Census.
## 3 Feature Extraction Methods and Indicator Development  
### Multi-Feature Extraction Methods in Time Domain  
Six statistical methods were adopted, including clearance factor, crest factor, impulse factor, kurtosis factor, shape factor, and peak acceleration, to extract multi-perspective features in the time domain. The mathematical expressions and explanations of these statistical approaches are presented in Table 1. A comparative analysis between the baseline track segment without interlayer debonding and the track segment with interlayer debonding was performed, and the performance of time-domain features is assessed in Section 4.2.
# Monitoring Perishable Commodities Using Cellular IoT:
## 2 Materials and Methods
### Field Test  
A field test was carried out with the complete system to verify that it operated correctly in terms of the wireless transmission of data to the server and its autonomy, along with the proper measurement of the parameters.  
For this purpose, the datalogger participated in the transport and distribution stage of perishable products in the postharvest stage by being installed at a strategic point in the climatic chamber of a moving refrigerated truck, being subjected to its temperature and humidity real conditions for two weeks. The temperature setpoints of the chamber were 4 \({}^{\circ}\)C and \(-19\) \({}^{\circ}\)C. In this case, a battery of 3000 mAh was used.  
The overdland transport was carried out by the company ESP Solutions (Avenida Espana, 14, Murcia, Spain) [70] through various points in Europe. The route followed by the datalogger inside the truck and the analysis of the communications performance can be seen in Section 3.5.
# An Overview of the IberSpeech-RTVE 2022 Challenges on Speech Technologies
## 5 IberSpeech-RTVE 2022 Results and Discussion
### Speaker Diarization and Identity Assignment Challenge
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**System ID** & **Primary** & **C1** & **C2** \\ \hline IV & 35.59\% & 45.92\% & 48.67\% \\ \hline IRTI & **18.47\%** & 19.58\% & - \\ \hline Best System 2020 & 34.29\% & - & - \\ \hline \hline \end{tabular}
\end{table} 
Table 13: DER (%) performance on the Albayzin 2022 Speaker Diarization Challenge. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Program** & **\#spkrs** & **MiSE (\%)** & **FASE (\%)** & **SpE (\%)** & **DER (\%)** \\ \hline \(3\times 4\) & 17 & 7.70 & 0.77 & 4.80 & 13.27 \\ \hline AG & 60 & 0.58 & 0.25 & 7.75 & 8.57 \\ \hline AT & 20 & 4.82 & 1.02 & 6.20 & 12.04 \\ \hline CA & 57 & 7.35 & 2.19 & 16.30 & 25.84 \\ \hline CO & 45 & 2.41 & 1.05 & 17.52 & 20.98 \\ \hline ED & 72 & 7.46 & 0.64 & 10.29 & 18.39 \\ \hline GR & 20 & 11.58 & 6.37 & 16.68 & 34.64 \\ \hline RD & 16 & 15.35 & 6.69 & 14.32 & 36.36 \\ \hline YR & 11 & 26.59 & 15.18 & 14.70 & 56.47 \\ \hline TOTAL & 5.86 & 1.57 & 11.03 & 18.47 \\ \hline \hline \end{tabular}
\end{table} 
Table 14: Speaker Diarization 2022 challenge, best system results in terms of the MiSE (missed speaker error), FASE (False Alarm Speaker Error), SpE (Speaker Error) and DER. \begin{table}
\begin{tabular}{c c c c} \hline \hline  & **MiSE (\%)** & **FAES (\%)** & **pSE (\%)** & **AER (\%)** \\ \hline IV (P) & 1.3 & 229.7 & 9.5 & 240.55 \\ \hline IV (C1) & 3.7 & 160.8 & 20.9 & 185.42 \\ \hline IV (C2) & 8.3 & 76.5 & 5.9 & 90.62 \\ \hline IV (C3) & 12.4 & 15.3 & 1.2 & 28.88 \\ \hline \hline \end{tabular}
\end{table} 
Table 15: Speaker Diarization and Identity Assignment 2022 challenge results in terms of MiSE (missed speaker error), FASE (False Alarm Speaker Error), SpE (Speaker Error) and AER.
## 4 Performance Evaluation
### Experiments
#### 4.1.1 Analysis on Effectiveness of Techniques Used for _HeaLow_
When a buffer hazard occurs, without _hazard state management_, a large portion of workload already in the work queue drops. In such situation, buffer intermittently rejects all workload for a short period. The network cannot handle such request, hence higher drop rate. By stabilizing the queue preemptively, the device can process comparably durable load for longer interval. Case 4 may also seem odd. Offloaded amount can also be dropped in either end of offloading process when deadline has passed. This means that a severe network bottleneck can cause packet drop even more severely. Hence, better network utilization can have a better success rate with the same amount of offload. Figure 4: The queue length and the number of offloaded workloads with respect to workload sequence number: (**a**) Case 1; (**b**) Case 2; (**c**) Case 3; (**d**) Case 4; and (**e**) Case 5. Especially in \(\times 10\) workload case, we can clearly see that Case 3 makes more offloading than Case 2 with preemptive queue stabilization. To avoid any confusion, \(\times\) notation means the number of audio streams that a device can process. The most important issue is that, with Cases 3, 4, and 5 on top of Case 2, the success rate increases without having more data offloaded. Since offloading consumes the computing resource of other devices and the network bandwidth, improving performance without network usage is always desirable. Through this experiment using real devices, we confirmed that _HeaLow_ with various techniques enables nodes to perform heavy-computation processes while satisfying a completion time requirement.
## 4 Enabling Technologies for FSO Communication
### Error Correction Codes and Modulated Signal Optimization
#### 4.7.1 Requirements for Channel Capacity-Approaching Transceivers  
To develop transceivers that approach the channel capacity in terms of transmission rate, three key requirements must be met. First, the symbol distribution should align with the channel's capacity-achieving distribution. Second, optimal, adequately long channel codes are necessary. Third, the transmission rate must be finely adjusted according to the channel conditions, meaning the encoder should support a wide range of transmission modes across various SNRs. However, designing efficient CM systems that meet these criteria is challenging. Typically, the input distribution does not match the capacity-achieving distribution, resulting in a shaping gap. Additionally, practical implementations use finite-length Forward Error Correction (FEC) codes, leading to a coding gap. Lastly, the number of modulation orders and coding rates available is restricted by system complexity, resulting in only partially adaptive systems [296; 297; 298].  
Concerning the shaping gap, optimizing the shape of the modulated signal constellation can reduce the discrepancy between the Shannon's limit and transmission rate. The primary methods of constellation shaping are geometric shaping and probabilistic shaping (PS). In geometric shaping, the constellation symbols are equiprobable but unevenly spaced. Conversely, a probabilistically shaped constellation features uniformly spaced symbols with differing probabilities assigned to each symbol. The latter has garnered significant attention in recent years owing to its rate adaptability, higher shaping gain, and the feasibility of employing Gray code for symbol labeling [296; 297; 299].
# A Novel Approach for the Estimation of Doubly Spread Acoustic Channels Based on Wavelet Transform
## Appendix A  
Let \(s(t)\) be the transmitted HFM signal and expressed as  
\[s(t)=\cos(-2\pi k\ln(1-\frac{t}{t_{0}}))\] (A1)The definitions of variables in (A1) are the same as in (4). In addition, we assume that the channel has \(L\) paths, with \(\{A_{l},a_{l},\tau_{l}\}\) as the path amplitude, Doppler scale factor and time delay of the \(l\)th path. Then the received signal without considering noise is as follows:  
\[r(t)=\sum_{l=1}^{L}A_{l}\cos(-2\pi k\ln(1-\frac{a_{l}(t-\tau_{l})}{t_{0}}))\] (A2)  
The instantaneous frequency of the received signal from \(l\)th path is  
\[f_{l}(t)=\frac{1}{2\pi}\frac{d}{dt}(-2\pi k\ln(1-\frac{a_{l}(t-\tau_{l})}{t_{0} }))=\frac{a_{l}k}{t_{0}-a_{l}(t-\tau_{l})}\] (A3)  
According to (8), there is a constant time shift \(\Delta t_{l}\) which makes the establishment of the following formula:  
\[\frac{a_{l}k}{t_{0}-a_{l}(t-\tau_{l})}=\frac{k}{t_{0}-(t+\Delta t_{l})}\] (A4)  
where \(\Delta t_{l}=\frac{(a_{l}-1)t_{0}}{a_{l}}-\tau_{l}\)  
At the receiver, we use the transmitted HFM signal as the mother wave. With the scale \(a_{W}\) and time shift \(b\), it can be written as  
\[\psi(t)=\cos(-2\pi k\ln(1-\frac{t-b}{a_{W}t_{0}}))\] (A5)  
In addition, the instantaneous frequency of \(\psi(t)\) is  
\[f_{\psi}(t)=\frac{k}{a_{W}t_{0}-(t-b)}\] (A6)  
Comparing with (A3), we can find that, by setting \(a_{W}=1\), WT coefficients will meet a peak at \(b=-\Delta t_{l}\).
## 3 Results
### Interpretation Results  
#### 3.2.1 Grad-CAM  
Regarding explainability, Grad-CAM was utilized in this research to provide interpretability to the RGB-CNN predictions. The Grad-CAM results were evaluated by a nuclear medicine specialist to strengthen the reliability of the results. For the implementation of Grad-CAM, the feature maps were extracted from the last convolutional layer of the RGB-CNN model and inserted into the Grad-CAM for the generation of heatmaps. The heatmaps demonstrate the impact of each pixel on the final prediction of RGB-CNN. In this study, the colormap entitled Jet was utilized, obtained from the OpenCV library, where low-impact pixel values are colored in blue and high-impact values are colored in red, as seen in Figure 3. Based on nuclear medicine experts, the Grad-CAM results offer interpretability and transparency of RGB-CNN's inner computations, regarding CAD diagnosis.  
In Figure 4, we demonstrate the Grad-CAM application to four pathological case studies, and in Figure 5, four normal case studies, related to CAD diagnosis. Regarding examples a and b, they correspond to correctly predicted instances, while c and d refer to cases that were falsely predicted. The different colors specify the importance of pixels, demonstrating the impact of the CNN classifier on individual pixels. Grad-CAM identifies the regions of interest and colors them in red. We observe that the Grad-CAM has provided exceptional and comprehensive results, where it correctly highlighted and detected the regions of interest.  
Figure 4: Representation of Grad–CAM application to pathological polar maps: (**a**) first TP case study, (**b**) second TP case study, (**c**) first False Positive (FP) case study, (**d**) second FP case study.  
Figure 3: Jet colormap color range demonstration.
## 3 Materials and Methods
### Data Preparation: Training, Validation, and Testing  
To fine-tune and assess the proposed ArCAR system, two different datasets are used; one for Arabic documents text classification as in Section 3.1.1 and another dataset for sentiment analysis as in Section 3.1.2. For both applications, the Arabic text files (i.e., documents or sentiments) for each class are randomly split into 70% in the training set, 10% in the validation set, and 20% in the testing set [24; 25; 27]. The trainable parameters (i.e., network weights and biases) of the proposed convolutional neural network are optimized via the training process using the training-validation sets. After that, the overall performance of the proposed deep learning model is evaluated utilizing only the evaluation set. In addition, the proposed model is assessed over five-fold tests for training, validation, and evaluation sets. These sets are generated via stratified partitioning ensuring an equal testing rate for each text document to prevent system bias. To build a reliable and feasible CAR system for Arabic text classification, it is important to use a k-fold cross-validation strategy, especially when the size of the dataset is not large enough for training purposes [23; 24; 25].
# Energy Reduction Multipath Routing Protocol for MANET Using Recoil Technique
## 2 Review of the State of the Art and Main Contributions of the Work
The author formulated a function based on route length and route energy to optimize the values of consumed energy using the binary particle swarm optimization (SWO) algorithm. The on-demand energy-based forwarding strategy (OEFS) enables the nodes to consume less energy but the route selection criterion is not based on reliability [2]. In [19], a protocol based on energy efficient routing was proposed, named AOMR-LM, which is an improvement on AOMDV [20]. Our approach discovers the route in such a way that it can reduce data redundancy and minimize the traffic overhead, which aids in the power conservation and leads to the enhancement of network life time. The concept of recoiled nodes further reduces the routing overhead whereas the last part of algorithm, namely the link reckoning algorithm, ensures break free packet transmission, lowers the packet loss and protects from inordinate delay in the network. Details of protocol are given in subsequent section of the paper. Figure 1: Illustrates the application of MANET in tactical network.
# Development of Additive Fibonacci Generators with Improved Characteristics for Cybersecurity Needs
## 1 Introduction  
Additive Fibonacci generators (AFG) are one of the types of pseudorandom sequence generators that are widely used in many technical means, particularly in cryptographic means of information protection. In their traditional design, they do not provide adequate cryptosevtivity, but can be used as part of cryptographic devices [1; 2; 3; 4; 5; 6; 7; 8; 9]. Recently, we proposed a modified AFG (MAFG), in which the introduction to their structure and additional logic circuit, allowed us to include, in the process of arithmetic addition, the result of a logical function from the binary values of the resulting register, which significantly improved the statistical characteristics of the pseudorandom output sequence [10; 11; 12; 13; 14; 15].  
At present, almost all classic AFGs and new MAFGs, designed for hardware implementation, operate according to recurrent equations with modules whose values are equal to the power of two. It simplifies their hardware implementation but narrows their functionality and worsens the statistical characteristics of the output pseudorandom sequences.  
In [16], we proposed AFGs that can work with an arbitrary value of a module, including a module whose value is a prime number. However, these devices do not have an additional logic circuit [10; 11; 12; 13; 14; 15], which does not allow satisfactory statistical characteristics to be obtained without the involvement of additional devices.  
In this article, we reveal the approach to constructing Fibonacci additive generators with modules of prime numbers. This construction method expands the capabilities of the hardware implementation of such generators and improves their output statistical characteristics, which allows them to be used effectively in cryptographic applications. A research methodology is proposed based on using abstract and hardware models of generators. Their identity is proved, which allows investigation of the statistical characteristics of such generators with the large values of arguments, which is especially important for cryptographic generators. The research results indicate that the proposed models and structures of generators can be effectively used to solve cryptographic problems of information security.  
The aim of the work is to create and research the characteristics of AFGs and MAFGs with modules whose values are prime numbers. To achieve this goal, new generator structures are proposed, in which the introduction of additional structural elements allows us to ensure the operation of generators with arbitrary values of the recurrent equation modulus. This is the scientific novelty of the obtained results, which significantly improves the statistical characteristics of generators, expands their functionality, and expands the scope of their use in cryptographic means of information protection, particularly in streaming ciphers.