esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 The Solution Proposed: WikiGIS
### WikiGIS Features
#### 3.3.2 Managing Traceability of the Geometric Component  
The WikiGIS manages three types of objects: points, polylines and polygons. The properties of these objects being different, particularly with respect to coordinate management, we identified three distinct families of entities:  
* The point, identified by a number, is characterized by its position (latitude and longitude), creation date, author and zoom level at which it becomes visible on the map. Other characteristics can be added, such as the type of icon or opacity.
* The polyline entity is characterized by the position of its tips, its creation date, author and zoom level at which it becomes visible on the map.
* The polygon entity has the same characteristics as the polylines.  
The WikiGIS allows dynamic storage of all the previous versions of the same object. As mentioned above, the previous versions and the current version of an object are considered as similar entities, since they have exactly the same type of properties. Only the values of these properties change. The current version of an object is the one corresponding to the latest creation date. In terms of visualization, when a user interacts with the map (zoom or pan), only the current version of the objects in the cartographic window appears. When the user clicks on one of these objects, then all the previous versions are displayed (in a different color).  
Figure 5 shows this principle. The series of top thumbnails displays the current version of the geometry. The series of bottom thumbnails represents the result of a click to activate the current geometry. The timeline is divided into three moments (...085,...152,...179) corresponding to three actions performed on the marker. Time in the database is counted in seconds since January 1, 1970. A \"timestamp\" is associated with each object. This solution offers unique and non-ambiguous object identification on the timeline. This is a quite usual convention in computer science (e.g., Linux). Of course, it is possible to convert each time expressed in \"seconds\" to \"day, month, year, hh, mm, ss\".  
* Configuration 1: The consultation date being earlier than the creation date of the point (...085), the query returns no object. The database has no point with a creation date earlier than the display date.
* Configuration 2: A current version of an object appears (object 16). When the user clicks on it, the icon turns green to indicate that it is active. No other marker appears at this stage, because there are no prior versions.
* Configuration 3: The object has been moved. Therefore, it has two versions. The green icon (object 17) indicates that the point is active. The earlier version (object 16) appears in blue. Itcorresponds to the current version in configuration 2. This geometry is associated with an earlier version in configuration 3.
* Configuration 4: the point has been moved again, which generated a new version of the object (object 18). At this stage, there are two earlier versions of the entity (object 16 and 17).
## 1 Introduction  
During the last years, the construction sector has been looking for some solutions able to reduce the environmental impacts typical of building materials, particularly related to the use of Portland cement, which requires a considerable amount of raw non-renewable natural resources and a great energy input during the clinker production [1].  
Several strategies have been proposed by different authors [2; 3] aimed at improving the sustainability of cementitious materials: reduction of the greenhouse gases emission by using alternative fuels [4; 5], replacement of Portland cement (OPC) clinker with low-carbon supplementary cementitious materials (SCM) [6; 7; 8], utilization of wastes [9; 10] and development of alternative binders (such as alkali-activated materials) [11; 12].  
One of the most interesting and simple ways to reduce the environmental impact of mortars and concretes is using calcium sulphoaluminate cement (CSA) as a partial replacement of ordinary Portland cement [13]. For this reason, ternary mixtures of OPC, CSA and calcium sulphate (gypsum, anhydrite or hemihydrate) were largely investigated and properties like rapid setting times, fast strength development and low shrinkage have been appreciated [14; 15; 16]. However, some aspects of CSA cement, i.e., the effect of admixtures, have not been exhaustively investigated yet.  
Most studies about admixtures in calcium sulphoaluminate cements deal with the influence of superplasticizers [17; 18; 19; 20; 21], shrinkage reducing admixtures [22; 23; 24] and retarders [16; 18; 25; 26; 27] but only a few papers focus on the use of hardening accelerating admixtures. [PERSON] et al. [28] highlighted the feasibility of using lithium chloride and lithium carbonate to accelerate the hydration of CSA-based mixtures thanks to the formation of Li-rich aluminum hydroxides. [PERSON] et al. also investigated the influence of Li-based admixtures (alone [29] or in combination with sodium borate [30]) on the hydration of CSA cements, confirming that the acceleration effect is related to the very rapid precipitation of amorphous Li-containing Al(OH)\({}_{3}\). However, there is a lack of knowledge regarding the physico-mechanical properties and durability issues of CSA-based mortars and concretes manufactured using Li-based hardening accelerating admixtures. On the other hand, to date, no experimental data have been published concerning the use of sodium-based accelerators in CSA-based mixtures.  
For the abovementioned reasons, the purpose of the present work is to evaluate the influence of hardening accelerating admixtures based on sodium and lithium carbonate on the rheological, physical, elasto-mechanical properties of mortars manufactured with calcium sulphoaluminate cement.
# Velocity-Aware Handover Self-Optimization Management for Next Generation Networks
###### Abstract  
The fifth generation (5G) network is an upcoming standard for wireless communications that coexists with the current 4G network to increase the throughput. The deployment of ultra-dense small cells (UDSC) over a macro-cell layer yields multi-tier networks, which are known as heterogeneous networks (HetNets). HetNets play a key role in the cellular network to provide services to numerous users. However, the number of handovers (HOs) and radio link failure (RLF) greatly increase due to the increase in the UDSC in the network. Therefore, mobility management becomes a very important function in a self-organizing network to improve the system performance. In this paper, we propose a velocity-based self-optimization algorithm to adjust the HO control parameters in 4G/5G networks. The proposed algorithm utilizes the user's received power and speed to adjust the HO margin and the time to trigger during the user's mobility in the network. Simulation results demonstrate that the proposed algorithm achieves a remarkable reduction in the rate of ping-pong HOs and RLF compared with other existing algorithms, thereby outperforming such algorithms by an average of more than 70% for all HO performance metrics.  
handover; HetNets; mobility management; self-optimization +
Footnote †: journal: Applied Sciences  
_Article_
## 4 Discussion
### The Correlation Between Sand Body Distribution and Fault
#### 4.2.1 Graben-Type Correlation Between Sand Body Distribution and Fault  
The graben-type association between the sand body distribution and fault consists of two dominant faults in the W-E direction and is a graben-like correlation of two normal faults with opposite dips. The lateral spreading range is large, principally distributed in the central part of the investigated area. The classical section is selected for analysis. It can be seen from the section that the strata thickness of the foot wall is thick and the difference between the left and right thickness is large. The fault growth index (Q) from left to right was 1.22 and 1.01, respectively. The fault distance is 107.4 m and 79.8 m, respectively.  
The graben-type link between the sand body distribution and fault is \"concave\". These two faults share a foot wall of fault. It is more prevalent in the major faults in the investigated area. The strike of these two faults is nearly perpendicular to the source direction, and the depression between these two faults provides a larger space for the sediments, which gives rise to a tremendous sediment accumulation in the depression, and the distribution pattern is tremendously affected by the strike of these two faults.
###### Abstract  
It is well known that existing empirical models cannot fit perfectly into environments other than those they were formulated in due to differences in terrain and climate. The Okumura-Hata family of models are gaining acceptability over the VHF/UHF channels. However, it is imperative to investigate their reliability and to use the one most suited to each environment. This study investigated the reliability of the Okumura-Hata, COST-231, ECC-33, and Ericsson models over digital UHF channels in Ikorodu and Akure, Southwestern Nigeria. The drive test protocol was used for data collection at intervals of 1 km along different routes from the experimental stations up to maximums of 10 and 16 km in Ikorodu and Akure, respectively. This was carried out for both wet and dry season months using a digital Satlink meter with a spectrum (WS-6936), GPS Map 78s and a field vehicle. The uniqueness of this study is that it used real-world data with a seasonal scope, and the mean values were employed in the analysis to strengthen the reliability of the results. The measured path loss (MPL) and predicted path loss (PPLM) were computed, with error margin analysis carried out between them. The results reveal a mean MPL of 110.42 dB in Ikorodu, while the PPLMs were 121.90, 123.55, 158.42, and 291.01 dB for the Hata, COST-231, Ericsson, and ECC-33 models, respectively. In Akure, the mean MPL was 123.157 dB, while the PPLMs were 121.922, 130.179, 198.979, and 313.494 dB. The results further indicate that the Hata model had the best performance with the lowest RMSE of 10.812 in Ikorodu, while COST-231 had the best performance in Akure, with the lowest RMSE of 9.877. The optimized Hata and COST-231 models were developed with improved RMSEs of 5.895 and 7.815 for the Ikorodu and Akure environments, respectively. The optimized models had higher degrees of reliability and will provide a valuable approach to wireless communication planning in tropical urban and suburban environments for achieving quality of transmission and reception (QoTnR) over UHF channels in Nigeria and similar environments in Africa.  
reliability; path loss models; optimization; RMSE; Hata; COST-231 and digital UHF channels +
Footnote †: journal:  
0000-0001-5000-0000-0000]Akinbolati
## 1 Introduction
### Organization of the Article  
This paper is organized as follows: in Section 2, the UWB antenna design, notching characteristics, coupling of notching elements with each other, and proposed MIMO design are presented in detail. In Section 3, the results are discussed comprehensively, i.e., S-parameter analysis, radiation patterns, peak gain and radiation efficiency. In Section 4, the diversity performance, fabrication, and measurement results are discussed. The article is concluded in Section 5.
# Creation, Analysis and Evaluation of AnnoMI, a Dataset of Expert-Annotated Counselling Dialogues +
## 5 Inter-Annotator Agreement (IAA)
### Results of Default IAA Measure  
All Fleiss'-kappa-based IAAs are listed in Table 9. Following [33], we group the IAAs into slight (0.01-0.20), fair (0.21-0.40), moderate (0.41-0.60), substantial (0.61-0.80), and almost perfect (0.80-1.00) agreement. We consider an attribute predictable if its IAA shows moderate or better agreement.  
We notice that for the utterance attributes where Binary and All(Strict) are applicable, the order of agreement is, without exception, All(Strict)-IAA \(<\) All-IAA \(<\) Binary-IAA, which proves the challenge of the subset used for computing All(Strict)-IAA, as well as the ease of annotating the absence/presence of a particular utterance attribute.  
The annotators show fair agreement on Input and Reflection under All(Strict), which reveals the difficulty of annotating these attributes despite their inclusion in MSC/MITI, particularly when their presence in an utterance cannot be easily ruled out. Nevertheless, the IAA jumps to substantial agreement for Input and Reflection under the Binary set  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Tierapist Utterance Attribute** & **IAA Setting** & **IAA** \\ \hline \multirow{3}{*}{Input} & All(Strict) & 0.34 \\  & All & 0.51 \\  & Binary & 0.64 \\ \hline \multirow{3}{*}{Reflection} & All(Strict) & 0.32 \\  & All & 0.50 \\  & Binary & 0.66 \\ \hline \multirow{3}{*}{Question} & All(Strict) & 0.54 \\  & All & 0.74 \\  & Binary & 0.87 \\ \hline \multirow{2}{*}{(Main) Behaviour} & All & 0.74 \\ \hline \multirow{2}{*}{**Client Utterance Attribute**} & **IAA Setting** & **IAA** \\ \hline \multirow{2}{*}{Talk Type} & All & 0.47 \\ \hline \hline \end{tabular}
\end{table}
Table 9: Inter-annotator agreements on utterance-level annotations, in Fleiss’ kappa. Orange, blue, cyan, and green indicate fair (0.21–0.40), moderate (0.41–0.60), substantial (0.61–0.80), and almost perfect (0.80–1.00) agreement, respectively.  
ting, which suggests the presence of distinguishable linguistic features unique to these two attributes.  
Encouragingly, Question, (Main) Behaviour, and Talk Type all record moderate or better IAAs under all settings, which shows the text-based predictability and therefore the existence of distinct linguistic features of these attributes.
## 2 Related Work
This review presents current trends and methodological developments in the field of water quality assessment. \begin{table}
\begin{tabular}{p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt}} \hline \hline
**Authors (Year)** & **Country** & **Dataset Size/Type** & **Model(s) Used \({}^{*}\)** & **Method Type** & **Conceptual Framework** & **Framework Proposed** & **Key Findings** \\ \hline [PERSON] et al. (2024) [20] & China & 6429 records; hourly measurements & LSTM, GRU + Bayesian optimization & Hybrid DL & BayesOpt + DL & Bayes-LSTM-GRU & RMSE reduced by 18.1\% vs. LSTM; time resolution critical \\ \hline [PERSON] et al. (2024) [11] & China & 20 datasets; statistical tests & Refined random forest & Supervised ML & Tree filtering & Refined random forest & Improved ensemble accuracy and diversity via pruning \\ \hline [PERSON] et al. (2024) [15] & China & 188,623 groundwater records & GBDT + MLP + attention & Hybrid DL & GBDT + attention MLP & BC-FAMLP & Accuracy 96.16\%, AUC 0.983; robust to noise \\ \hline [PERSON] et al. (2024) & Indonesia & 20,132,022 records; weekly measurements & ANN, LSTM & Supervised DL & Time series models & ANN \textgreater{} LSTM & ANN \textgreater{} 0.00436, better potability accuracy \\ \hline [PERSON] et al. (2024) [19] & Pakistan & 79 records from Swat and Panjkora & RF, SVM, MLP, DT, and MN & Supervised ML & Boruta + ML & RF \textgreater{} SVM \textgreater{} MLP \textgreater{} DT \textgreater{  
In summary, the reviewed literature suggests two main approaches to improve deep learning performance on tabular water quality data: (1) the use of attention-based architectures capable of modeling complex relationships between features, e.g., transformer models, TabNet, and SAINT; and (2) the application of unsupervised or semi-supervised pre-learning methods to extract robust feature representations from unlabeled data. Accordingly, this paper presents a unified evaluation framework that includes attention mechanism-based models (FTITransformer and SAINT) together with a newly developed
# ShuffleDetect: Detecting Adversarial Images against Convolutional Neural Networks
## 9 Intrinsic Performance of ShuffleDetect\({}^{\mathbf{C},R_{th},t}\)
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{\(R_{th}\)} & \multicolumn{2}{c}{**Untargeted**} & \multirow{2}{*}{**Metrics**} & \multirow{2}{*}{**\(\mathcal{C}_{1}\)**} & \multirow{2}{*}{**\(\mathcal{C}_{2}\)**} & \multirow{2}{*}{**\(\mathcal{C}_{3}\)**} & \multirow{2}{*}{**\(\mathcal{C}_{4}\)**} & \multirow{2}{*}{**\(\mathcal{C}_{8}\)**} & \multirow{2}{*}{**\(\mathcal{C}_{6}\)**} & \multirow{2}{*}{**\(\mathcal{C}_{7}\)**} & \multirow{2}{*}{**\(\mathcal{C}_{8}\)**} & \multirow{2}{*}{**\(\mathcal{C}_{9}\)**} & \multirow{2}{*}{**\(\mathcal{C}_{10}\)**} & \multirow{2}{*}{**Avg**} \\  & \multicolumn{2}{c}{**Attack**} & & & & & & & & & & & & \\ \hline \multirow{11}{*}{**FGSM**} & **DR** & 69.7 & 64.9 & 78.7 & 83.6 & 78.5 & 77.7 & 85.5 & 86.7 & 85.8 & 78.7 & **78.9** \\  & **TP** & 67 & 63 & 78 & 82 & 77 & 77 & 83 & 85 & 85 & 78 & **77.5** \\  & **FP** & 23 & 13 & 13 & 18 & 12 & 13 & 11 & 8 & 22 & 14 & **14.7** \\  & **FN** & 29 & 34 & 21 & 16 & 21 & 22 & 14 & 13 & 14 & 21 & **20.5** \\  & **Precision** & 0.74 & 0.82 & 0.85 & 0.82 & 0.86 & 0.85 & 0.88 & 0.91 & 0.79 & 0.84 & **0.83** \\  & **Recall** & 0.69 & 0.64 & 0.78 & 0.83 & 0.78 & 0.77 & 0.85 & 0.86 & 0.85 & 0.78 & **0.78** \\  & **F1** & 0.71 & 0.71 & 0.81 & 0.82 & 0.81 & 0.8 & 0.86 & 0.88 & 0.81 & 0.8 & **0.8** \\ \hline \multirow{11}{*}{**FGD Inf**} & **DR** & 45.7 & 63.4 & 74.0 & 53.7 & 56.9 & 68.8 & 58.7 & 63.0 & 61.7 & **60.7** \\  & **TP** & 38 & 52 & 60 & 43 & 49 & 53 & 47 & 58 & 55 & **50.6** \\  & **FP** & 11 & 13 & 16 & 9 & 12 & 10 & 8 & 17 & **11** & **11.9** \\  & **FN** & 45 & 30 & 21 & 37 & 37 & 24 & 33 & 34 & 34 & **32.8** \\  & **Precision** & 0.77 & 0.80 & 0.78 & 0.82 & 0.80 & 0.84 & 0.85 & 0.77 & 0.83 & **0.8** \\  & **Recall** & 0.45 & 0.63 & 0.74 & 0.53 & 0.56 & 0.68 & 0.58 & 0.63 & 0.61 & **0.6** \\  & **F1** & 0.56 & 0.7 & 0.75 & 0.64 & 0.65 & 0.75 & 0.68 & 0.69 & 0.7 & **0.68** \\ \hline \multirow{11}{*}{**FGD Inf**} & **DR** & 40.8 & 40.6 & 64.5 & 75.0 & 66.6 & 56.1 & 74.7 & 62.1 & 68.4 & 44.6 & **59.3** \\  & **TP** & 38 & 37 & 62 & 72 & 62 & 55 & 71 & 59 & 65 & 42 & **56.3** \\  & **FP** & 20 & 11 & 13 & 16 & 9 & 12 & 10 & 8 & 16 & 11 & **12.6** \\  & **FN** & 5
# Applying Machine Learning to DEEC Protocol: Improved Cluster Formation in Wireless Sensor Networks
### Clustering Techniques for WSNs
Future clustering research will use ML, energy harvesting, and secure protocols, and clustering will also adapt to new technologies like the Internet of Things (IoT) and 5G networks because clustering is necessary for WSNs to work well. It helps save energy, makes networks easier to manage, and helps them last longer. Clustering is a technique that reduces the energy consumption of Wireless Sensor Networks (WSNs), and the clustering technique divides nodes into clusters with a cluster head (CH) [7]. A CH gathers data from the nodes and transmits it to the base station, thus conserving energy and improving the network's lifetime. K-NN and K-Means are some clustering algorithms used to improve DEEC's performance. DEEC is suitable for WSNs with heterogeneous nodes, so CHs are selected based on the residual energy. K-Means and K-NN are ML techniques; therefore, they can be used for various purposes [8]. The k-means algorithm clusters nodes according to their location or other characteristics, and the K-Nearest Neighbour (K-NN) algorithm clusters nodes according to their similarity with their nearest neighbour node [9]. A variety of clustering techniques can be used to improve clustering in WSNs. Thus, this leads to improvements in the lifetime and performance of networks. Figure 1 shows the clustering process in WSNs.
## 4 Experiments and Analysis
### Analysis and Comparison of Measurement Methods  
The common methods for blade twist measurement are analyzed and compared as shown in Table 5. It can be concluded as follows:  
(1) CMM collects blade coordinates point by point using a contact probe and the measurement accuracy can be improved through path planning and sampling optimization, but is limited by the measuring principle and radius of probe ball. The CMM method is insufficient in adaptability, efficiency and edge measurement accuracy.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \multicolumn{2}{c}{**Leading Edge**} & \multicolumn{3}{c}{**Trailing Edge**} \\ \cline{2-6}  & \(R_{q}\) & \(x_{1}\) & \(y_{1}\) & \(R_{h}\) & \(x_{2}\) & \(y_{2}\) \\ \hline Test profile I & 0.461 & \(-\)48.858 & 10.848 & 0.422 & 7.27 & \(-\)11.58 \\ Test profile II & 0.527 & \(-\)33.699 & 1.951 & 0.54 & 23.176 & \(-\)5.013 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Parameters of the leading and trailing edges measured by JE42 (unit: mm).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameters** & **Specifications** \\ \hline Range & X: 400 mm, \(Y\): 500 mm, \(Z\): 650 mm, C: 360\({}^{\circ}\) \\ Resolution & \(X\), \(Y\), \(Z\): 0.1 \(\upmu\)m, C: 0.0002\({}^{\circ}\) \\ Total accuracy & \(\leq\)2 \(\upmu\)m \\ \hline \hline \end{tabular}
\end{table}
Table 3: Parameters of four-coordinate measuring machine.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **CMM of Global Series** & **CORE DS from** & **Form-Free and** \\  & **from HEAXGON** & **WENZEL** & **High-Precision Method** \\ \hline \multirow{2}{*}{**Methods of detection**} & \multirow{2}{*}{Contact measurement} & Non-contact optical measurement & Non-contact and \\  & & & measurement & form-free measurement \\ \hline \multirow{3}{*}{**Adaptability**} & Poor adaptability, theoretical model is necessary & Poor adaptability, theoretical model is necessary & Good adaptability, self-constructing theoretical model \\ \hline \multirow{2}{*}{**Efficiency**} & \multirow{2}{*}{Low efficiency} & Higher efficiency due to & The efficiency is further \\  & & & optical scanning & improved by optimizing \\ \hline \multirow{2}{*}{**Accuracy for edge detection**} & Serious fluctuation or & & Less than 10 \(\upmu\)m due to a \\  & even loss of measured & 10 \(\upmu\)m level & priori planning \\ \multicolumn{2}{c}{data} & & measurement method \\ \hline \hline \end{tabular}
\end{table}
Table 5: Analysis and comparison of common methods for blade twist measurement.  
(2) CORE DS collects coordinates efficiently by scanning the blade profile using the laser sensor and the measurement accuracy can reach 10 \(\upmu\)m level through inclination error compensation. However, it still has some problems in adaptability, evaluation algorithm and measurement accuracy.  
(3) The method for the blade twist measurement proposed in the study is form-free, efficient and accurate, and can solve the problems of high-precision measurement and evaluation for the twist of aeroengine blade primely.