esa-sceva/satcom-chunk-collection huggingface dataset:
# Effect of Formation Size on Flocking Formation Performance for the Goal Reach Problem
## 3 Results
### Experimental Results
#### 3.2.1 Experimental Results in an Environment without Obstacles
Cohesions per member: The average cohesion steps per member monotonically increases with the increase in the flock size as shown in Table 3, which is confirmed by the scatter plot in Figure 5f. The distribution of data points in the scatter plot shown in Figure 5f shows that the standard deviation of number of cohesion steps is similar for each flock size; however, the average number of cohesion steps increases as the flock size increases. The number of cohesion steps needed to keep a larger flock size in cohesion is larger than the number needed for a smaller flock size. The \(p\)-value obtained in each of the six curve estimation models was less than 0.001, which means that the sample data provides enough evidence to reject the null hypothesis, and that changes in flock size are significantly associated with shifts in the number of cohesion steps on the population level. The highest \(R^{2}\) value is that associated with the cubic function and explains 87.2% of the variation in the number of cohesion steps due to shifts in the flock size. Task completion time and goal localization window: The time required to complete the task in minutes (Figure 5g), as well as the goal localization window (Figure 5i) all increased along with an increase in flock size, as supported by the average value readings from Table 3. The \(p\)-value obtained in each of the six curve estimation models for both of these metrics was less than 0.001, which means that the sample data provides enough evidence to reject the null hypothesis and that changes in flock size are strongly associated with shifts in each of these two performance measures. For the time required to complete the task measure, the highest \(R^{2}\) value was associated with the exponential function and accounted for 80.2% of the variation in time required to complete the flocking task due to changes in the flock size. As for the goal localization window, the highest \(R^{2}\) value was associated with the cubic function and indicates that 75.5% of the variation in the goal localization window is explained by changes in the flock size, while the \(R^{2}\) value associated with the quadratic function closely follows at 75.2%. These results are in line with the shape of the points on the scatter plot in Figure 5i. As previously mentioned, the flocking behavior became stable when the majority of the flock members applied the alignment rule, rather than the attraction or separation rules. Based on the obtained results, the larger the flock size, the larger the number of repulsion and cohesion steps applied. The simulation outputs of two randomly selected experiments with flock sizes of 30 and 100 are shown in Appendix C.
# Iterative Named Entity Recognition with Conditional Random Fields
## 4 Discussion  
### General Model Performance and Individual Entity Recognition  
The initial strong improvement to the model performance of the reference experiment (and subsequent experiments) is attributable to the growing number of annotated entities used to fit the model during each iteration so that the model learns from more data. Additionally, the introduction of annotated, particularly 'unsure' sentences to the model training dataset provides an additional source of relevant model information in each iteration. The manually annotated sentences were particularly long, containing at times over 100 tokens per sentence, and encompassing a large number of entities. Through the annotation of many entities with high uncertainty, the model parameter space is expanded in regions lacking information in the prior iteration, enabling efficient information growth for the model in particularly uncertain regions in the parameter space. Thus, our results confirm previous findings highlighting the importance of data selection for model performance beyond the benefit of having large collections of documents, for instance, using bootstrapping procedures [27].  
The comparatively lower performance of the model in recognising entities from the ENAMEX group can be attributed to the higher diversity of these entities in natural language usage than for entities from the NUMEX and TIMEX groups. NUMEX and TIMEX entities tend to have standard formats that can be well-estimated via regular expressions, whereas ENAMEX entities can be comprised of a vast, indeed nearly limitless, number of combinations of words, (invented) names, or acronyms. Thus, our results agree with previous reports of reduced vocabulary transfer of entities of the ENAMEX group, especially for persons and organisations [7; 28; 29]. Some regular expressions, such as the acronym GmbH for proprietary limited companies, can be used to identify entities in the German language, but not all entities can be identified using such expressions and not all texts relating to such an entity use the appropriate acronym. The poor performance of the reference experimental model at recognising the TIMEX entities after a large number of annotated tokens can be attributed to its rare occurrence in the data: in the evaluation dataset, only twelve instances were manually annotated.
# Exploring Synergy of Denoising and Distillation: Novel Method for Efficient Adversarial Defense
## 3 Distillation and deNoise Network(DiNo-Net)  
We propose a robust defense model, the DiNo-Net: Distillation and deNoise Networks, that does not adhere to the characteristics of obfuscated gradients. DiNo-Net maximizes the effect of feature denoising using distillation. In this study, we applied a knowledge distillation technique to learn the features of adversarial robustness indirectly. By utilizing feature denoising, we propose an effective defense model for adversarial attacks that curtails the increase in the number of model parameters and learning time. Figure 3 shows the configuration of the proposed model (DiNo-Net). As shown in Figure 3, DiNo-Net deploys two defense techniques to effectively learn adversarial robustness. In the subsequent sections, this study provides a detailed description of these two methodologies for enhancing defense performance. Finally, this chapter explains the factors that led to the enhanced performance of our proposed defense technique compared to existing techniques.
## 4 Results and Discussion
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \multicolumn{4}{c}{**Testing Set (30\%)**} \\ \hline
**Labels** & **Accuracy** & **Precision** & **Recall** & **Specificity** & **F-Score** \\ \hline
1 & 99.76 & 98.68 & 98.68 & 99.87 & 98.68 \\
2 & 99.64 & 99.30 & 96.58 & 99.93 & 97.92 \\
3 & 99.88 & 100.00 & 98.59 & 100.00 & 99.29 \\
4 & 99.58 & 99.31 & 95.97 & 99.93 & 97.61 \\
5 & 99.82 & 100.00 &The training loss (TRL) and validation loss (VLL) obtained by the PODCNN-LWID algorithm with the test dataset are displayed in Figure 7. The experimental outcome indicates that the PODCNN-LWID method exhibits minimal TRL and VLL values. In particular, the VLL is less than the TRL. A clear precision-recall inspection of the PODCNN-LWID algorithm with the test dataset is portrayed in Figure 8. The figure denotes that the PODCNN-LWID approach results in enhanced precision-recall values under all classes. Figure 8: Precision–recall analysis of PODCNN-LWID approach. Figure 7: TRL and VLL analysis of PODCNN-LWID approach. A brief ROC investigation of the PODCNN-LWID algorithm on the test dataset is displayed in Figure 9. The results indicate that the PODCNN-LWID method exhibits its ability in categorizing distinct classes in the test dataset. Table 5 highlights the intrusion detection efficacy of the PODCNN-LWID model in terms of distinct measures. The experimental results indicate that the PODCNN-LWID model reports enhanced results. For instance, based on \(accu_{y}\), the PODCNN-LWID model offers an increased \(accu_{y}\) of 99.57%, whereas the CNN-MCL, XGB, RF, SVC, ANN, and E-ML models accomplish decreased \(accu_{y}\) values of 99.09%, 99.15%, 99.02%, 99.26%, 99.12%, and 99.48%, respectively. In contrast, based on \(sens_{y}\), the PODCNN-LWID approach renders an increased \(sens_{y}\) of 97.65%, whereas the CNN-MCL, XGB, RF, SVC, ANN, and E-ML algorithms establish decreased \(sens_{y}\) values of 97.19%, 96.89%, 96.06%, 96.21%, 97.01%, and 96.20%, respectively. Finally, based on \(spec_{y}\), the PODCNN-LWID technique has an increased \(spec_{y}\) of 99.76%, whereas the CNN-MCL, XGB, RF, SVC, ANN, and E-ML approaches accomplish decreased \(spec_{y}\) values of 99%, 99.23%, 99.60%, 99.11%, 99.21%, and 99.31% respectively. From the results and discussion, it is apparent that the PODCNN-LWID model accomplishes the maximum intrusion detection performance, with an accuracy of 99.57%. The enhanced performance of the PODCNN-LWID model is due to the inclusion of the PO-algorithm-based hyperparameter tuning process. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Methods** & **Accuracy** & **Sensitivity** & **Specificity** \\ \hline PODCNN-LWID & 99.57 & 97.65 & 99.76 \\ CNN-MCL & 99.09 & 97.19 & 99.00 \\ XGB & 99.15 & 96.89 & 99.23 \\ RF & 99.02 & 96.06 & 99.60 \\ SVC & 99.26 & 96.21 & 99.11 \\ ANN & 99.12 & 97.01 & 99.21 \\ E-ML & 99.48 & 96.20 & 99.31 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Comparative analysis of PODCNN-LWID approach with existing algorithms. Figure 9: ROC analysis of PODCNN-LWID approach.
## 5 Discussion
### Limitations and Future Work  
Although the proposed method exhibits strong positioning accuracy and robustness across various scenarios, it still has certain limitations. First, the method is highly dependent on the quality of feature matching, and its performance in highly dynamic or uncontrolled environments has not been fully validated. Specifically, in real-world applications--such as in busy urban areas or environments with significant lighting variations--the method's robustness and adaptability require further evaluation. Second, the current method entails high computational complexity, which, particularly in real-time applications, could create computational bottlenecks, limiting its practical deployment in efficient autonomous driving systems.  
Future research should focus on several directions for improvement. A primary area for development is optimizing the algorithm to enhance real-time performance. Research aimed at reducing computational load through hardware acceleration, parallel computing, or the design of more efficient algorithms could facilitate broader application in real-time positioning systems. Additionally, the practical implementation of the algorithm should emphasize the development of user interfaces to improve system usability and practicality. Future work could also explore multi-sensor data fusion, such as integrating LiDAR and IMU data, to improve the system's performance in complex environments, particularly under dynamic conditions and adverse weather, thus further enhancing the method's reliability and adaptability.
## 3 Results and Discussion
### Relationships between Environmental Factors and Microbial Community  
Correlations between predominant microbial taxa (top 10 genera with relative abundance of bacterial and fungal communities) and environmental factors (air temperature, air humidity, warehouse temperature, and warehouse humidity) were investigated (Figure 6). Air temperature showed positive correlations with _Pseudarthrobacter_ and _Allorhizobium-Neorhizobium-Parahizobium-Rhizobium_, and warehouse temperature positively correlated with _Muribaculaceae_, _Vicinarinobacteraceae_, and _Pseudomonas_ (Figure 6A). Warehouse humidity and air temperature showed positive correlations with _Methylobacterium-Methyldorbium_, _Lactobacillus_, and _Pseudocitrobacter_ (Figure 6A). _Cladosporium_ and _Nigrospora_ were linked to the varieties of rice grains stored under the condition of normal temperature, horizontal ventilation, and nitrogen gas atmosphere (Figure 6B). _Aspergillus_ and _Moesziomyces_ were linked to the varieties of rice grains stored under the condition of normal temperature, vertical ventilation, and nitrogen gas atmosphere. In this storage condition, more attention to mycotoxin production should be considered. _Phaeosphaeria_ was the contributor to rice grains stored under the condition of quasi-low temperature, horizontal ventilation, and nitrogen gas atmosphere (Figure 6B). Warehouse temperature showed positive correlations with _Nigrospora_ and _Phaeosphaeria_ (Figure 6B). Air temperature showed positive correlations with _Candidia_, _Moesziomyces_, and _Aspergillus_. _Ustilaginoidea_, _Alternaria_, _Fusarium_, and _Cladosporium_ and air humidity showed positive correlations (Figure 6B). Among these environmental factors, air temperature is closely related to some toxigenic filamentous fungi. During rice grain storage, air temperature should be taken into consideration.  
Figure 5: Correlations between bacterial (**A**) and fungal community (**B**) and rice grains’ quality, including moisture content, starch content, fatty acid value, protein composition, volatile compounds (VIP > 1), and mycotoxin content (_p_ < 0.05).  
The network was constructed to analyze the interaction patterns of co-occurrence or co-exclusion among microbial community individuals in different conditions (Figure 6C,D). _Sphingomonas_, _Vicinamibacteraceae_, and _Muribaculacea_ showed positive correlations. _Aspergillus_, _Alternaria_, _Cladosporium_, and _Fusarium_ showed positive correlations. Some studies have shown that _Sphingomonas_ can produce anticyanobacterial compounds argimicins to form an antagonism with _Escherichia coli_, _Enterobacter_ aerogenes, etc., which can also slow down food corruption [59]. _Aspergillus_, _Alternaria_, and _Fusarium_ can produce toxins and endanger the quality and safety of rice grains [60]. In the future, more attention should be paid to the changes in the abundance of these genera.
# Enhancing Energy Efficiency in IoT-NDN via Parameter Optimization
## 4 Framework Architecture
### Modeling the Parameters
#### 4.1.4 Caching Scheme  
A _caching scheme_ is an algorithm that decides whether an incoming element should be added to the Content Store in the first place. Typically, caching algorithms (e.g., in CPU caches) cache every element, but in networking, this is not necessarily desirable. For example, assume a Data packet has to travel \(n\) nodes from the producer to the consumer. If that packet is cached on every node, it will be present in all \(n\) Content Stores in between. Hence, there is a lot of redundant data on many neighboring nodes. It is, however, often more reasonable not to cache every incoming element, leaving space for more different packets on neighboring nodes. An example of such a scheme is the _probabilistic scheme_, but for comparison, one can also use the schemes \(CEE\) and _no caching_, which will be discussed next.  
_CEE (Caching Everything Everywhere)_ is a naive scheme that always caches every element. While it minimizes the number of hops to access an element, there is less space for other elements and less data diversity.  
While _no caching_ neglects one key feature of NDN and cannot be expected to lead to lower energy consumption, it can be useful to use it as a baseline to compare other schemes against.  
_Probabilistic caching_ is a simple algorithm that uses a fixed caching probability across the network: Given a probability \(p>0\) for the entire network, each Content Store will cache an incoming element with probability \(p\). This has the major advantage that no extraneous communication is required to exchange caching information between nodes, which would increase the energy consumption. When using this scheme, it can be expected that data in the network will be distributed better than with CEE.
## 2 Methods  
This is a pilot, randomized, double-blind, controlled clinical study that followed the guidelines set out in the Consolidated Standards of Reporting Trials (CONSORT) conducted with patients diagnosed with type 2 diabetes mellitus.  
The research was carried out in the Laboratorio de Cinesioterapia e Recursos Terapeuticos Manuais (LACIRTEM) of the Physiotherapy Department of the Universidade Federal de Pernambuco (UFFE). This study was approved by the Research Ethics Committee of the CCS/UFPE (No.3.219.332) and registered at www.clinicaltrials.gov (NCT04207853), respecting the ethical aspects of Resolution 466/12 of the National Health Council and the Declaration of Helsinki. All participants signed the free and informed consent form (FICF) after being informed of the objectives, risks, and benefits of the research.  
The study was disseminated in various ways to attract volunteers (social networks, leaflets, lectures, scientific events, etc.). Volunteers were instructed to contact the research team to receive information and study guidance. When the volunteer met the research criteria, they were instructed to attend the day and time scheduled for exams to confirm the disease, while wearing appropriate clothes for the study.  
The study included 20 clinically classified type 2 diabetic men (three) and women (17) who were aged 60 to 80 years, functionally independent, without severe foot deformities requiring the use of therapeutic footwear, without orthopedic deficiencies, without indication of deep vein thrombosis, without the use of walking aids, and with cognitive ability to respond and perform the exercises assessed by the Mini-Mental State Examination (MMSE), using 19 points as the cutoff point for illiteracy [20].  
Volunteers who performed another physical activity and those who presented peaks of hypertension during treatment or who reported discomfort during the session that prevented the completion of training were discontinued.  
Randomization was performed by a researcher not involved in any research stage, using the tool offered by www.randomization.com, and the order of participants was placed in numbered, sealed, and opaque envelopes, which were only opened at the time of the survey intervention. Thus, participants were randomly divided into two groups: whole-body vibration training (WBV), simulated vibration training (sham), and a single treatment session for WBV and sham. All participants underwent an initial and final evaluation.  
Initially, anthropometric data were collected: gender, age, body mass index (BMI), vital signs (heart rate (HR), blood pressure (BP), and peripheral oxygen saturation (SpO\({}_{2}\)), capillary glucose, and clinical history at the time of evaluation. Subsequently, the volunteers were assessed for peripheral circulation through blood flow and peripheral skin temperature on both limbs and then instructed to perform training on the vibrating platform. At the end of the intervention, blood flow, peripheral skin temperature, vital signs, and capillary blood glucose were re-evaluated.
# Intuitionistic and Interval-Valued Fuzzy Set Representations for Data Mining
## 2 Background
### Uncertainty Representations
#### 2.1.2 Interval-Valued Fuzzy Sets  
Interval values are used in many areas to capture the imprecision and uncertainty of data. We first provide the formalisms for interval arithmetic [16; 17; 18] as needed for interval-valued fuzzy sets. We let \(\mathrm{D}\) be the domain, and intervals are represented by the values of the lower bound, \(\mathrm{z_{t}}=\mathrm{lb}(\mathrm{a_{i}})\), and an upper bound, \(\mathrm{z^{t}}=\mathrm{ub}(\mathrm{a_{i}})\), of an interval \(\mathbf{I}\) (\(\mathrm{a_{i}}\)) for the data value \(\mathrm{a_{i}}\in\mathrm{D}\)  
\[\mathbf{I}\,(\mathrm{a_{i}})=[\mathrm{z_{t}},\,\mathrm{z^{t}}]=\{\mathrm{z}\in \mathrm{D}\,\mid\,\mathrm{z_{t}}\leq\mathrm{z}\leq\mathrm{z^{t}}\} \tag{2}\]  
Now, an interval-based fuzzy set representation, \(\mathrm{IVF}(\mathrm{D})\), is based on using upper, \(\mathrm{mu}(\mathrm{a_{i}})\), and lower bounds, \(\mathrm{ml}(\mathrm{a_{i}})\), on fuzzy memberships  
\[\mathrm{IVF}(\mathrm{D})=\{<\mathrm{a_{i}},\,\mathbf{I}\,(\mathrm{a_{i}})>\, \mid\,\mathbf{I}\,(\mathrm{a_{i}})=[\mathrm{ml}(\mathrm{a_{i}}),\,\mathrm{mu}( \mathrm{a_{i}})]\} \tag{3}\]  
For an interval \(\mathbf{I}\) (\(\mathrm{a_{i}}\)), the size or length of the interval, \(\mathrm{IW}\), is the difference of the lower and upper bounds,  
\[\mathrm{IW}\,(\mathbf{I}\,(\mathrm{a_{i}}))=\,\mid\mathrm{ml}(\mathrm{a_{i}}) \,-\,\mathrm{mu}(\mathrm{a_{i}})\,\mid \tag{4}\]  
\(\mathrm{IW}\) is often used as a representation of the uncertainty of a data value \(\mathrm{a_{i}}\) in an IVF as an information measure [19; 20].
## 3 Results and Discussion
### Fresh Properties and Density of OMGPC
#### 3.1.2 Workability of OMGPC  
Figure 7 shows that incorporating R5% and R10% improves the workability by 5.6% and 13.7%, respectively, while G5% and G10% enhanced the workability of OMGPC by 15.8% and 26.3%, respectively, compared to the control mix. The improved workability of the OMGPC incorporating REIN8 is attributed to the hydrophobic nature of plastic. However, the OMGPC mixes with FWG aggregates possess higher workability than specimens with REIN8 aggregate. This improved workability of concrete containing FWG over plastic-containing mixtures is also reported in a study performed by [PERSON] et al. [34]. Further, enhanced workability in concrete containing FWG aligns with the previous findings [54; 55; 56; 57]. As obtained from the physical characterisation of aggregates, the water absorption of sand, REIN8, and FWG are 1.42%, 0.63%, and 0.23%, respectively, hence contributing to the improved workability of the FWG-contained OMGPC mixtures compared to others. In addition to the lower water absorption property of FWG aggregate compared to REIN8 and sand, [PERSON] et al. [57] noted that the improved workability is attributed to the smooth surface of the FWG aggregate, allowing free water flow in the  
Figure 6: Influence of Na\({}_{3}\)PO\({}_{4}\) on setting time of geopolymer binder.  
mixture. Hence, substituting natural sand with these recycled aggregates would reduce the water demand for concrete production.