esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Numerical Analysis and Results
### Case Study I
#### 4.1.1 Service 1  
We have categorized our services with all the different criteria as defined below. The first service is to be initialized from the data center (DC1) to the data center (DC2). The controller helps to select the optimal network as per their preferences being decided for different criteria defined above, such as Energy Efficiency (EE), Bandwidth (B), Delivery probability (DV), and Delay Tolerance (DT). The utility values have been defined for all attributes in Section 2, above.  
\[S_{1}=<EE,BDV,DT> \tag{20}\]  
In this Service \(1(S_{1})\), we assume that this service is for non-real-time applications, such as video surveillance data, that are accumulated at the data center and that can be delayed for up to 13 hours. There are three possible networks to choose from: UMTS, WLAN, and public transport. When the end-user sends and receives big data files, such as backup storage or large datasets, in TB or PB, this scheme applies. The scheme is, thus, more or less delay-tolerant, for example, background downloading of email messages, sending of data with Google Grive, and data backup. In this case, we give more importance to the energy-efficiency factor than other attributes, as we can bear delays for these applications or services. Now, the same procedure will be followed for all the attributes as per services, as defined in Tables 3-5.  
Figure 11: Auckland Central map with locations.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Attributes**} & **Energy Efficiency** & \multirow{2}{*}{**Bandwidth**} & **Delay Tolerance** & **Delivery Probability** \\ \hline
**Energy Efficiency** & 1 & 7 & 9 & 3 \\ \hline
**Bandwidth** & 1/3 & 1 & 7 & 2 \\ \hline
**Delay Tolerance** & 1/9 & 1/7 & 1 & 1/5 \\ \hline
**Delivery probability** & 1/3 & 1/2 & 5 & 1 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Pairwise Comparison Utility matrix as per importance scale.  
\(\lambda_{max}=4.178069312\); CI = 0.059356437; CR = 0.065951597 \(<\) 0.1  
This pairwise matrix also passes a consistency check, which means that priority is selected correctly.
## 4 Impact of the Constellation Mesh Area on Localization Accuracy
### Experimental Site  
The scenario under consideration is a typical small office environment with a total area of 7.5 m by 6 m, for a total area of 45 m\({}^{2}\), with the usual furniture. Its map is reported in Figure 9a, and it is possible to see that it includes furniture, metallic objects and office closets spread around the area. The tests were performed while allowing the office employees to continue their habitual tasks. The latter condition results to affect the variance of the observed data, \(\sigma^{2}\), defined in the Equation (7). The picture of the test site is reported in Figure 9c where we observe the single constellation rectangular mesh installed on the ceiling, with the three out four anchors visible, and the mobile tag installed on a tripod. This installation guaranties a line-of-sight between the anchors and the mobile tag, which is a requirement for a proper positioning estimation.  
The main goal of this work consists on the experimental investigation of the relation between the rectangular mesh size and the position estimation accuracy, thus making the anchor nodes location a degree of freedom to design the network. In general, a system architect designer can consider other degrees of freedom like the number of anchors and their arrangements in different mesh shapes, being the latter having already been considered by an idealized point of view in the previous Section 3.2.  
To fully evaluate the effects of the anchor distribution on the localization accuracy, the mobile tag position has been estimated on a large number of points spread all over the room; these are visible in Figure 9a. In particular, the grid is composed by 299 points regularly spread throughout the room, wherever it was possible compatibly with the obstacles present in the environment, and spaced by 0.5 m \(\times\) 0.5 m both by row and column, with points on adjacent columns vertically shifted by 0.25 m. The three test anchor constellations are visible in Figure 9b, they are characterized by different edge thus area sizes, namely a \"large\", a \"medium\" and a \"tiny\" mesh configurations; Table 1 summarizes the constellation parameters for each case. Overall anchors were hanged at the ceiling, at height \(Z_{\text{i}}\) = 2.8 m.  
Figure 8: Calculation of the average CRB for different mesh shapes and corresponding sizes, as a function of (**a**) the mesh edge size, and (**b**) the mesh area; \(\sigma^{2}\) = 1.
# Fuzzy Logic-Based Geographic Routing Protocol for Dynamic Wireless Sensor Networks
## 4 Simulation and Complexity Analysis
### Comparison of Network Performance
#### 4.2.2 Routing Overhead Ratio  
The network routing overhead ratio refers to the ratio of routing packets to the total packets in the simulation process, and a lower routing overhead ratio indicates that the routing algorithm converges faster. In this paper, the routing overhead includes the cost of obtaining the location of the destination node.  
Figure 11 shows the impact of maximum speed on the routing overhead ratio in a dense network (\(n=140\)).  
As shown in Figure 11, the routing overhead of the GPSR, GPSR-AD, LPESG, and FLGR protocols increases gradually with the increase in maximum speed. Compared with the GPSR, GPSR-AD, and LPESG protocols, the routing overhead of the FLGR protocol reduces significantly. This reduction occurs because in the former three protocols, the location information of the destination node should be obtained through the GLS algorithm, increasing the routing overhead. For the FLGR protocol, although the number of the routing hop is larger than that of the GPSR protocol (as shown in Figure 8), the GLS algorithm is not needed. Hence, the routing overhead decreases considerably, compared with the other protocols.  
Figure 12 shows the impact of node number on the routing overhead ratio in a slowly changing network (\(v_{\max}=20\)).  
Figure 11: Impact of maximum speed on routing overhead ratio with \(n=140\).  
As shown in Figure 12, the routing overhead ratios of the four protocols decrease with the increases in node number. Compared with GPSR, GPSR-AD, and LPESG protocols, the routing overhead ratio of the FLGR protocol is significantly reduced. Although the packet delivery ratios of the LPESG and FLGR protocols are almost the same in a slowly changing network (as shown in Figure 10), the LPESG needs additional routing overhead through GLS algorithm, as is the case with the GPSR and GPSR-AD protocols.
prevalence has increased and with it, the capability of imaging technologies used for clinical assessment [2].  
Dual-energy X-ray absorptiometry (DEXA or DXA) is used to determine bone density by combining two X-ray beans with different energy levels [3; 4; 5; 6]. DEXA is used to monitor osteoporosis and assess fracture risk in mostly post-menopausal women [7]. From 1994 on, T-score (standard deviations compared with a young adult--reference population) has been used as a scale to classify bone mineral density (BMD) measurements in postmenopausal women. The T-scores evaluated were: (1) health: T-score value greater or equal to \(-1\); (2) osteopenia: T-score value between \(-2.5\) and \(-1\); (3) osteoporosis: T-score value less or equal to \(-2.5\). This classification was later adopted for all men, women and children by the International Society for Clinical Densitometry [7]. DEXA is regarded as the gold standard technique to assess BMD and despite being widely disseminated; it is scarcely used in Dentistry [7].  
Computed Tomography (CT) has several uses in bone analysis and has been used in healthcare and diagnostic investigation, so it may be used to evaluate osteoporosis [1]. In dentistry, Cone Beam Computed Tomography (CBCT) scanners are increasingly used to evaluate morphologic information [8]. CBCT can additionally aid in bone density estimations through a linear correlation between CBCT voxels values (usually in Hounsfield units) and bone mineral content [8]. Knowing bone mineral density is crucial in some fields of dentistry such as Implantology [9]. To use CBCT for bone mineral density assessment, this exam must present with equal accuracy to the gold-standard technique (DEXA) [3].  
If a strong correlation coefficient is established between DEXA and CBCT, this suggests that a CBCT can be used to evaluate patients with low BMD with the same level of assurance as DEXA. In dentistry the CBCT is commonly used to more accurately help plan future interventions, but it can be additionally used to detect low bone density without additional scans (e.g., DEXA), thus avoiding exposure to any additional radiation whilst reducing costs and saving time. The opportunity for osteoporosis diagnosis using CBCT has been reported in literature, albeit sparsely.  
The aim of this systematic review is to identify, appraise and synthesize all available evidence regarding the correlation between Dual Energy X-ray Absorptiometry and Cone Beam Computed Tomography techniques in the evaluation of bone mineral density.
# Detecting Cyber Threats in UWF-ZeekDataFall22 Using K-Means Clustering in the Big Data Environment
## 5 K-Means Clustering Results and Discussion
### Finding the Ideal Number of Clusters
#### 5.4.1 First Method: Clusters with at Least One Attack as Attack Clusters  
From the previous set of experiments, specifically Table 5, since the best results were obtained with a seed of 600 for clusters with at least one attack data record, the seed of 600 was used for the following set of experiments. And, looking back at Table 2 and Figure 2, the best results were obtained using clusters of 200 and higher, hence a starting point of 200 clusters was used for this experimentation.  
Analysis of the First Method:  
* Performance Improvement with More Clusters
* As the number of clusters increased, precision, recall, and accuracy improved, reaching a peak effectiveness at 350 clusters.
* At 350 clusters, the model achieved 97.58% precision and 97.45% recall, providing high accuracy while minimizing false positives.
* In terms of computational time, although the lower number of clusters (300 clusters or less) had lower computational times and the higher number of clusters (above 300 clusters) had higher computational times, there was no definite pattern.
* Optimal Number of Clusters
* At 400 clusters, a slight performance drop was observed, suggesting that further segmentation would not enhance classification.
* 350 clusters were chosen as the optimal cluster count for this method, balancing detection accuracy and computational efficiency.  
* A seed of 600 with 350 clusters will produce the best set of results for clusters with one attack. The best results are bolded in Table 8.
# An Unsupervised Deep Learning System for Acoustic Scene Analysis
## 3 Experiments
### Comparison Methods and Parameter Setting  
The proposed method was compared with the state-of-the-art JOLRSC method [9]. In the JOLRSC method, the MFCC feature was extracted and flattened, and then fed into the online low-rank subspace clustering (OLRSC). Specifically, both the original feature and the feature in the low-rank subspace are used for joint clustering.  
To investigate the effects of the binaural representation and the auto-encoder-based monaural system on performance separately, we further studied the average-channel component of JASEC, named _monaural auto-encoder network with spectral clustering_ (MAESC). MAESC first extracts MFCC from only average channel; then uses an auto-encoder network to extract a bottleneck feature; and finally adopts spectral clustering and AHC for clustering. We compared MAESC with SSC [15] and OLRSC [9], since all of them operate on the average channel of the stereo audio clips.  
For a fair comparison, we adopted the same data preprocessing and feature extraction procedure as that in [9,15]. Specifically, the audio clips were resampled to 16 kHz. The frame length was set to 256 and the hop size was set to 160. We extracted 12-dimensional MFCCs and their one order and first and second difference coefficients, which formed 36-dimensional features.
# Evaluating the Impact of Pre-Configured Uplink Resources in Narrowband IoT
## 3 Simulation Environment
It is worth mentioning that resource allocation was predetermined offline before the simulation begins. This pre-allocation is necessary because the eNodeB assigns resources to a UE for a fixed duration, determined by a timer. However, the timing of packet transmissions by the UE, based on the assigned PUR resources, is adjustable. Once a UE has valid PURs, it can transmit data directly to eNodeB. In addition, it is worth mentioning that setting up a UE with PURs can be achieved in two ways. First, the UE can initiate the process by sending a PUR request to an eNodeB. Alternatively, the network can trigger the configuration based on factors like subscription details or the recognition of a regular traffic pattern. In our simulations, we only considered the PURs triggered by a UE, as shown in Figure 8.  
Figure 8: A sequence diagram for a UE-initiated PUR procedure. Figure 6: The LENA-NB protocol stack with the resume/suspend extension of [PERSON] et al. [11], CIoT-OPT, EDT, and our extension of the stack with PUR. Figure 7: A message sequence diagram depicting the process of packet transmission and acknowledgment during the PUR procedure within the LENA-NB simulation environment.
## 6 Societal and Economic Impacts
### Increased Efficiency  
AI technologies can reduce operational costs by minimizing reliance on manual inspections and increasing sorting efficiency. [PERSON] et al. [22] emphasized how hybrid models could optimize recycling pathways, enabling industries to recover maximum value from waste textiles while minimizing environmental impact. Such advancements align closely with circular economy goals, driving sustainability in the textile sector.
## 2 Materials and Methods
### Frequency Band Selection
We successively tested these outdoor nodes for underground-to-underground data transmissions under various operating conditions and radio parameters. The results showed stable packet reception between the RX (receiver) and TX (transmitter) nodes operating at 433 MHz, with no packet losses.
# Fast Prediction and Optimization of Building Wind Environment Using CFD and Deep Learning Method
## 3 Results and Discussions
### Flow Field Prediction Analysis  
The qualitative representation of the error between the predictions of the deep learning model and the CFD computational results is shown in Figure 6. The velocity values of the building airflow field computed by CFD and the deep learning model are compared, and the error is calculated with  
\[V_{error}=V_{CFD}-V_{CNN} \tag{8}\]  
where \(V_{CFD}\) represents the actual wind speed value computed by CFD and \(V_{CNN}\) represents the predicted value of the deep learning model. If \(V_{error}\) is 0, it indicates that the predicted values of the model match the CFD computational results.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline \(V\) (m/s) & 4, 5, 6, 7 \\ \(H\) (m) & 40, 45, 50, 55, 60, 65, 70 \\ \(\theta\) (\({}^{\circ}\)) & 0, 15, 30, 45, 60, 75, 90 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Summary of parameter set of building layouts and boundary conditions in CFD simulations.  
The results in Figure 6 demonstrate that the deep learning model aligns well with the CFD computational results in predicting the building airflow field. The error plot illustrates the exact discrepancies between the deep learning model and the CFD computational results. It can be observed that the overall flow characteristics of the building airflow field, as predicted by the deep learning model, are in good agreement with the CFD computational results. The errors are primarily concentrated in the flow separation regions near the edges of the building. This may be attributed to the complex flow conditions near the building edges, which are challenging to predict accurately. Nevertheless, the deep learning model has effectively learned the specific features of the building airflow field.  
Figure 5: MSE loss in training and validation process.  
Figure 6: Comparison between CFD simulation results and deep learning model predictions.  
By comparing the CFD computational results with the predictions of the CNN model, as shown in Figure 7, a difference is made between the results of the average velocity of the wind field calculated by CFD and predicted by the CNN model. The red line represents the linear regression line of the CFD computational results, while the light red area represents the 95% prediction interval of the predicted values. It can be observed that the data points are mainly distributed within the 95% prediction interval, indicating that the CNN model can accurately predict the velocity values of the CFD-computed airflow field. There are only a few points outside the prediction interval, which can be attributed to differences in wind speeds resulting in variations in flow characteristics.
## 1 Introduction
* Tradeoff between detection speed and accuracy. Faster detection speed sometimes implies that the scale of the network model is small, resulting in light-weight feature extraction. However, the larger model improves the recognition accuracy, but it wants high computing power and so the detection speed is slow, which cannot meet the amount of desired time of the instrumentation with very little computing power in IoT. To optimize the above four issues, we propose an Intelligent Fast Detection for Real-time Image Information in Industrial IoT (IFD). The innovation points are as follows:  
* An Intelligent Fast Detection for Real-time Image Information in industrial IoT is proposed, which adopts the improved YOLOv3-tiny framework and can meet the detection speed necessities of a real-time system and effectively improve the detection accuracy. * We distinguish whether an object belongs to a smaller object according to the number of pixels occupied by the object in the image, and then, we use WANG to expand the dataset of smaller objects. We expanded the amount of smaller objects in the dataset but reduce the larger objects. This is to reduce the preference for larger objects in network training. * The k-means++ clustering algorithm is employed to obtain the predetermined boundary box to enhance the accuracy of the classification results. Among the many algorithm frameworks based on CNN, the YOLO framework is famous for its fast detection speed [19]. In the evolution of the Yolo algorithm series, the most important change between versions is the change of backbone. You can choose the version of YOLO suitable for the actual environment by replacing the backbone of YOLO, so its flexibility is suitable for industrial production. In this paper, the main reasons why our IFD model adopts the YOLOv3 family are as follows: (1) YOLOv3 does not pursue speed so much but rather pursues its performance on the basis of maintaining real-time performance. (2) Compared with the fact that there is no residual structure in the backbone (Dark-net19) of YOLOv2, YOLOv3's Dark-net53 can achieve the same effect as resnet-152. (3) YOLOv2 performs tensor size transformation (image size transformation) in the forward propagation process through pooling operation, which will lead to a serious loss of image information in the forward propagation process. However, YOLOv3 uses convolution, which can extract more abundant image information than pooling operation. The rest of this text is organized as follows: Section 2 introduces the work conducted by previous researchers. Section 3 details an outline of our own work. Firstly, the mechanism of VaryBlock and also the methodology of group action VaryBlock and YOLO-tiny square measure are introduced. Then, we introduce a way to extend the dataset to complement the linguistics information of smaller objects. Afterwards, we introduce the utilization of k-means++ to come up with a planned bounding box. Section 4 provides the experimental results to verify the period of time accuracy of our planned methodology in object detection. Finally, a short conclusion and future analysis square measure are given in Section 5.
## 2 Materials and Methods
### EEG Data Collection  
Multichannel resting-state EEG signals were acquired with 128 Ag/AgCl electrodes (Electrical Geodesics Inc., Eugene, OR, USA). Continuous EEG signals were amplified, digitized with a sampling rate of 1000 Hz, and collected employing Net Amps 300 amplifier and Net Station 4.5.2 software (Electrical Geodesics Inc., Eugene, OR, USA) on a Mac PC. Prior to recording, channel gains and zeros were measured to provide an accurate scaling factor for the display of waveform data. The children's head size and Cz was measured and marked with a wax pencil to ensure the right size and accurate placement of the net. Scalp impedances were checked online using Net Station (EGI, Inc.) to ensure the scalp impedances below 50 K\(\Omega\). Data were referenced online to Cz. Ten minutes of \"resting-state\" open-eye EEG were recorded while children were comfortably seated in on an armchair or on a caregiver's lap in a dimly lit room. According to the standard international 10-20 electrode placement, we selected 19 electrodes (i.e., Fpl, Fp2, F3, F4, C3, C4, P3, P4, O1, O2, F7, F8, T3, T4, T5, T6, Fz, Cz, and Pz) for a more accurate result.
# Data Fusion Analysis and Synthesis Framework for Improving Disaster Situation Awareness
## 7 Algorithms for Synthesizing the Optimal Data Fusion Configuration
### Best-Fit  
This is a 'greedy' algorithm that builds up a search space of possible element/data-source compositions step by step. At each step, the data source which has the best effectiveness value is kept. The other alternatives are discarded. This process terminates until all prospective data sources are considered and/or the cost and timing constraints are violated. According to this approach, the data sources which are selected until the termination phase give the optimal composition for fusion. The Algorithm 1 is implemented by the function _CALCULATE_BEST_FIT_. The parameters used in this algorithm are _instance_, _time_limit_, and _cost_limit_, which represent an instance of a physical object, timing and cost constraints, respectively. Lines (2) to (4) are used to create the initial setting of the search tree, which represents the possible alternatives of the data sources attached to a given instance. Here, _instance_ represents a geographical element such as an instance of class Residence. Selecting the best data source at each step is realized by calling on the function _BEST_APPLICABLE_SOURCE_ in line (5). This function returns the best data source at a given step. This data source is appended to the design tree as a mode. The total cost and timing values are calculated and updated. This process continues until the time or cost limits are violated or all data sources are considered (line 12). The function _BEST_APPLICABLE_SOURCE_ searches for all possible data sources for the given context of the instance and the related tree structure. It accepts the parameters _ds-tree_, _instance_, _time_limit_, and _cost_limit_ and returns the data source that has the maximum contribution to the effectiveness value. The function _CALCULATE_EFFECTIVENESS_ implements the formulas given in Section 6.2. **Example 3**: _Let us assume that the best data fusion composition must be found for an instance of class Residence. The following characteristics are assumed:_  
* _The cost limit is_ \(15\) _K in unit of currency._
* _The time limit is_ \(3.6\) _K in seconds._  
Figure 5: Estimated effectiveness value of the example with respect to objective \(1\).