esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Performance Assessment  
### Simulation Analysis  
#### 4.1.1 Simulation Setup  
The UWA channel is one of the most complicated channels, and there is still no closed formula to express it. To verify the validity of the proposed method in UWA environments, a real UWA channel response measured in the experiment carried out in September 2014 at Songhua Lake, Jilin, China, is used for simulation analysis. In the Songhua Lake experiment, the transmit sensor and the receiver hydrophone were suspended from two ships moored on the shore. The transmit sensor was deployed 1 m below the surface, and the receiver hydrophone was placed 0.5 m below the surface. The water depth at the transmitter and the receiver was about 4 m, and the communication distance was 350 m. The measured discrete-time channel response is shown in Figure 4. The FMT modulation UWA communication using traditional MMSE adaptive equalization was also analyzed in the same simulation condition for comparison with the proposed method. To express conveniently, the FMT modulation UWA communication using traditional MMSE adaptive equalization is abbreviated to the method using traditional MMSE adaptive equalization. The parameters for simulation analysis are shown in Table 1.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameters** & **The Proposed Method** & \begin{tabular}{c} **The Method Using Traditional** \\ **Minimum Mean Square Error** \\ **(MMSE) Equalization** \\ \end{tabular} \\ \hline Communication band \(B\) (kHz) & 8–16 & 8–16 \\ \hline The number of subbands of filtered & \(8\) & \(8\) \\ multitone (FMT) modulation \(M\) & \(8\) & \(8\) \\ \hline Roll-off factor of each transmit filter \(\alpha\) & \(0.5\) & \(0.5\) \\ \hline Symbol interval \(T\) (ms) & \(1.5\) & \(1.5\) \\ \hline Mapping pattern & \
\begin{tabular}{c} Binary phase shift keying (BPSK) \\ 8 phase shift keying (8 PSK) \\ \end{tabular} & BPSK, 8 PSK \\ \hline Number of information bits on each \\ subband & \(750\) (BPSK), \(2250\) (8 PSK) & \(1500\) (BPSK), \(4500\) (8 PSK) \\ \hline Error control coding (ECC) &
\begin{tabular}{c} Convolution code with generator \\ polynomial [7; 5] \\ \end{tabular} & None \\ \hline Number of mapped symbols on each \\ subband & \(1500\) & \(1500\) \\ \hline Symbol rate on each subband & \(667\) & \(667\) \\ (symbol/s) & \(333\) (BPSK), \(1000\) (8 PSK) & \(667\) (BPSK), \(2000\) (8 PSK) \\ \hline The number of equalization & \(25\) & \(25\) \\ coefficients & \(25\) & \(25\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Parameters for simulation analysis.
# Novel Fuzzy Logic Scheme for Push-Based Critical Data Broadcast Mitigation in VNDN
## 2 Vehicular Named Data Networking (VNDN)
### Pending Interest Table (PIT)  
Pending Interest Table (PIT) is another data structure of the VNDN architecture. PIT contains all the requested interest packets received and forwarded in the search for a data packet. Each PIT records the name or prefix of the data carried by the interest packet of its incoming and outgoing interface(s). After successfully delivering the data packet, the entry is removed from the PIT table.
## 6 Future Directions  
Although the present research addresses many of the issues on privacy-preserving data aggregation, there are still many research areas which must be studied. Here we list some important ones. First, the current trend of research on privacy-preserving data aggregation considers static networks. But many real life scenarios, such as sensing the health status of athletes and patients, require dynamic networks. Therefore, privacy-preserving data aggregation in dynamic environments is a possible future research direction. Second, many critical decisions are made on the basis of the aggregated results obtained. Therefore, the aggregated results should be free from malicious modifications of sensor data, _i.e._, false data injections, by any compromised node (particularly by a compromised aggregator). Detecting false data injections in WSNs is a challenging task. Hence, the development of a privacy-preserving data aggregation scheme that avoids the problem of malicious modifications of sensor data on their way to the sink could be an interesting issue for future research. Third, since sensor data is highly correlated, data aggregation can be achieved by employing source coding techniques. Using the idea of source coding may seamlessly integrate data privacy and aggregation. For this reason, there is significant scope for future work on privacy-preserving data aggregation based on source coding. Fourth, data aggregation and integrity checking threaten privacy preservation of sensor data because for supporting comparative aggregation functions, especially _Min/Max_ functions, data aggregators need to know the raw data of the sensor nodes for data integrity checking. In this way, individual sensors data are disclosed to other sensor nodes, thus resulting in the violation of privacy preservation. Therefore, it
# Low-Complexity Convolutional Neural Network for Channel Estimation
## 2 5G NR System Model
It is possible to configure a double DM-RS structure which supports up to four reference-signal instances per slot. This flexibility is mirrored also in the frequency domain, where two different types of the reference signals can be configured. Type 1 consists of mapping up to four orthogonal signals using a single-symbol DM-RS and up to eight orthogonal reference signals using the double-symbol DM-RS. In type 2, the maximum allowed numbers are six and twelve, respectively. In order to ensure an accurate estimation of the resource grid in both domains for the channel, the two configurations can be combined [20]. Some of the possible combinations are illustrated in Figure 1.  
At the receiver, the DM-RS location in frequency and time is communicated by the Downlink Control Information (DCI). The comparison between the received reference signal and the known reference signal generates the channel knowledge for specific resource elements. Using the channel estimation module, this knowledge is expanded and applied to the whole resource grid, generating the channel matrix. Despite the working principle being straightforward, the channel estimation is one of the most complex modules to be implemented due to variability of the channel over time and frequency [21].
# Three-Dimensional Pulp Volume Analysis in Lip and Palate Cleft Population
## 1 Introduction
Some studies have considered pulpal volume as a parameter for age estimation, but none have focused on a population with cleft lip and palate (CLP) [9; 11; 21]. Cleft lip and palate is the most common craniofacial malformation, representing 1 in every 500 to 1000 births worldwide. It has been reported in the literature that teeth close to the cleft region are likely to present some degree of malformation (in size, shape or number) and are commonly associated with developmental delays or asymmetric formation timing, which could affect pulp volume [22]. According to [PERSON] et al. [23], CLP patients often present a greater number of teeth with an enlarged pulp volume when compared to non-CLP individuals. Moreover, the dental anomalies present in these CLP patients may serve as clinical markers for sub-phenotypes. As these markers are more accurately identified, clinicians will be more alert to follow up with genetic testing [24; 25; 26]. The awareness of pulp volume in CLP patients is essential to execute some clinical procedures, as well as age estimation of this population. As it stands, the aim of this study is to determine the changes in the pulp volume of maxillary central incisors in patientswith and without CLP. The null hypothesis refers to the non-existence of differences in the pulp volume of patients with and without CLP.
## 3 Experimental Regime
### Analysis of Adhesion Strength by Adhesion Area
#### 3.6.3 AR 100% Specimen Results  
For the AR 100% specimens, out of the 40 tile pieces of the specimen, 40 (100% of the entire tile surface) of No.1 specimens showed stable adhesion, and the pieces that satisfied the ISO standard requirement (0.5 N/mm\({}^{2}\)) were 30 (75% of the stable attached pieces). For the No. 2 specimens, 36 (90% of the entire tile surface) showed stable adhesion, and 28 (77.8% of the stable attached pieces) satisfied the ISO standard requirement. For the No. 3 specimens, 36 pieces (90% of the entire tile surface) showed stable adhesion, and the pieces that satisfied the ISO standard requirement were 32 (88.9% of the stable attached pieces). The overall average adhesion strength of No.1, No. 2, and No. 3 specimens were, respectively, 0.78 N/mm\({}^{2}\), 0.73 N/mm\({}^{2}\), and 0.76 N/mm\({}^{2}\), resulting in a comprehensive average of 0.76 N/mm\({}^{2}\). When considering the average of only the pieces with stable adhesion, the average was, respectively, 0.78 N/mm\({}^{2}\), 0.85 N/mm\({}^{2}\), and 0.85 N/mm\({}^{2}\), resulting in a comprehensive average of 0.83 N/mm\({}^{2}\). Refer to Table 9 and Figure 16 below.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline
**Specimen Number** & **Coordinates** & **x1** & **x2** & **x3** & **x4** & **x5** & **x6** & **x7** & **x8** & **Avg.** & **Result** \\ \hline \multirow{6}{*}{1 st Specimen} & y1 & 0.32 & 0.74 & 0.79 & 0.93 & 0.76 & 0.66 & 0.71 & 0.23 & 0.64 & \multirow{6}{*}{40 Pieces Measured} \\  & y2 & 0.36 & 0.94 & 0.80 & 0.89 & 1.09 & 0.70 & 1.16 & 0.41 & 0.79 & \\  & y3 & 0.42 & 1.11 & 1.05 & 1.18 & 1.07 & 0.89 & 0.97 & 0.43 & 0.89 & \multirow{6}{*}{10.78 N/mm\({}^{2}\)} \\  & y4 & 0.32 & 1.29 & 0.97 & 1.31 & 1.26 & 0.85 & 1.00 & 0.29 & 0.91 & \\  & y5 & 0.46 & 1.01 & 0.70 & 0.72 & 0.64 & 0.85 & 0.64 & 0.33 & 0.67 & \\  & Avg. & 0.38 & 1.02 & 0.86 & 1.01 & 0.96 & 0.79 & 0.90 & 0.34 & 0.78 & \\  & y1 & 0.53 & 0.73 & 0.40 & 0.81 & 0.82 & 0.81 & 1.00 & 0.60 & 0.71 & \\  & y2 & 0.41 & 1.04 & 0.00 & 1.00 & 1.08 & 0.99 & 0.98 & 0.48 & 0.75 & \\  & y3 & 0.19 & 1.16 & 1.20 & 1.07 & 1.12 & 1.12 & 0.46 & 0.92 & \multirow{6}{*}{10.73 N/mm\({}^{2}\)} \\  & y4 & 0.00 & 0.84 & 1.22 & 0.78 & 0.83 & 1.25 & 0.96 & 0.30 & 0.77 & 0.73 N/mm\({}^{2}\) \\  & y5 & 0.00 & 0.81 & 0.85 & 0.28 & 0.26 & 0.58 & 1.10 & 0.00 & 0.49 & 36 Pieces Measured \\  & Avg. & 0.23 & 0.92 & 0.73 & 0.79 & 0.82 & 0.95 & 1.01 & 0.37 & 0.73 & \\  & y1 & 0.00 & 0.81 & 0.79 & 0.88 & 0.81 & 0.74 & 0.85 & 0.57 & 0.68 & \\  & y2 & 0.32 & 1.01 & 0.89 & 1.02 & 1.11 & 0.87 & 1.04 & 0.69 & 0.87 & \\  & y3 & 0.46 & 1.18 & 1.09 & 1.16 & 1.05 & 1.03 & 1.01 & 0.34 & 0.92 & \multirow{6}{*}{10.76 N/mm\({}^{2}\)} \\  & y4 & 0.24 & 1.04 & 0.92 & 1.05 & 1.15 & 0.92 & 0.9 & 0.00 & 0.78 & \\  & y5 & 0.00 & 0.79 & 0.74 & 0.81 & 0.69 & 0.77 & 0.71 & 0.00 & 0.56 & \\  & Avg. & 0.20 & 0.97 & 0.89 & 0.98 & 0.96 & 0.87 & 0.90 & 0.32 & 0.76 & \\ \hline \hline \end{tabular}
\end{table}
Table 9: AR 100% specimen result.  
Figure 15: AR 80 \(\pm\) 2% specimen adhesion strength per piece coordinate (average).
# Around View Monitoring-Based Vacant Parking Space Detection and Analysis
## 4 Experimental Results
### Summary  
In this paper, an AVM-based method to detect vacant parking spaces was proposed. To test its viability in real-life settings, we performed experiments in indoor parking lots, outdoor parking lots, and roadside parking spaces. Table 5 shows that the parking space detection algorithm had high recall and precision rates in outdoor settings. In comparison, the algorithm was less effective at detecting parking spaces in indoor settings because of factors such as lighting and columns. However, the algorithm had a high precision rate for detected spaces. Table 6 shows that the proposed space occupancy classification algorithm achieved favorable classification rates in both indoor and outdoor settings. In particular, integrating both types of feature extraction methods produced the lowest error rates. Because our algorithms are based on visual detection of vacant parking spaces, lighting can cause serious problems that must still be resolved. Using the system at night is another obstacle that awaits solutions. In the future, we hope to use active sensors to improve the robustness of the system.
## 1 Introduction
However, no previous works have been found in the literature aimed at characterizing the geometric shape of the collectors or their degree of modularity in terms of optimizing the performance of a photovoltaic installation in the event of the possible incidence of shading. In this context, in the present work, the annual radiative uptake has been quantified in a wide set of PV installations with dual-axis monitoring with different geometries in which the shape of the collectors and the design parameters have been systematically varied. For each case, it has been assumed that the collectors follow the optimal solar tracking strategy proposed by [PERSON] et al. [37; 38]. Unlike traditional solar tracking methods that search for the position of the sun at each moment using astronomical models [39; 40; 41], according to this strategy, the collecting planes are oriented at each instant of time towards the direction of space in which the irradiance is maximum, except during the moments when such orientation implies the shading of one collector over another. When this occurs, normally at the beginning and end of the day, the collectors are oriented in the direction in which, without causing shading, the incident irradiance on the collectors is maximum. In this way, the production of each installation, which is considered proportional to annual solar radiation, is calculated under the hypothesis that each installation will follow an optimal tracking strategy adapted to its own geometry. Therefore, this study aims to advance the characterization of the electrical behavior of shaded solar trackers, which is an issue where the scientific community has made a considerable effort, simulating tracking strategies for an improvement in photovoltaic production [12; 14; 42]. To achieve this objective, after this introduction in which the scientific advances made by the scientific community in the field of solar tracking in PV plants are presented, the following section describes the methodology followed in this study to simulate annual solar radiation incident on collectors with a dual-axis tracking strategy that optimizes radiative uptake while avoiding shading between collectors. Similarly, the methodology established for the study of the influence of the design parameters of a PV plant on this annual solar irradiance is explained. Based on this, Section 3 presents the results when applying the methodology described to an existing PV plant (\"El Molino\", Cordoba, Spain) with its design parameters systematically modified. Similarly, an adjustment model is proposed that represents in a simplified manner the dependence of the annual solar irradiance with respect to the design variables studied, and these dependencies are quantified. Finally, in Section 4, the main conclusions of the present study are presented.
## 3 An Integrated Tolerance Theory for the Kinematic Accuracy Analysis of Landing
### The Integrated Tolerance Analysis Procedure  
As described in Section 3.1, six constitute elements exist in a small displacement screw model. Each element for the final functional requirement must be formed as:  
\[\delta_{k}=\sum_{i=1}^{n}\sum_{i=1}^{6}J_{ijk}\delta_{ij}\;(k=1\cdots 6) \tag{15}\]  
Following the concepts in robust design, the ranges of \(\delta_{ij}\) can be easily controlled by the designer and they can be treated as control factors. The ranges of these tolerances are taken as levels of control factors. Although the kinematic accuracy model is adopted and clearances are taken as functional element pairs, there are still uncountable uncontrollable factors, such as temperature, deformation. Consequently, all other factors which cannot be controlled are noise factors.  
For joint clearances in a mechanism, the integrated tolerance analysis procedure is described as follows.  
* Establish the kinematic accuracy model of a mechanism and deduce the propagation function for the FR,
* According to the experience and engineering handbook, conduct primary tolerance design and analysis,
* Select control factors and their corresponding levels, and choose an orthogonal array as an experiment matrix,* Perform the experiments and calculate \(S/N\) ratios,
* Analyze the \(S/N\) ratios, and obtain the optimum setting of control factors,
* Verify the obtained factors and conduct tolerance redesign and reanalysis.  
The Tolerance Design and Robust Study for the Joint Clearances of a Landing Gear Retraction Mechanism  
In this section, a typical landing gear retraction mechanism is chosen as an example to illustrate the methodology proposed. In Section 4.1, primary tolerance design and analysis are carried out. To decrease the fluctuation of the errors, the integrated theory proposed is used in Section 4.2. In Section 4.3, a revised tolerance design is given, and the analysis completed.  
The retracted and extended position of the landing gear is shown in Figure 3. AV is the main struct; OP is the actuator cylinder and serves as the driving component; CD and BC are the drag braces; KL and KC are the lock links; QR is the lock actuator; DE, EF, FG, GH, and HI constitute a linkage mechanism, which connects the hatch door.  
When the landing gear is fully retracted and extended, DC, CK, and KL make up the lock mechanism, which maintains the landing gear in the retracted and extended position, respectively. However, the landing gear is not stable when it approaches the retracted and extended position. The problem is strongly related to the kinematic accuracy of the mechanism and the joint clearances are indispensable.  
According to the contact theories which are summarized by [PERSON], two assumptions must be described before beginning analysis:  
* There is no deformation because of contacts in joints.
* Contacts between the hole and shaft are supposed to be line contact.
# LoRa Technology Propagation Models for IoT Network Planning in the Amazon Regions
## 3 Measurement Methodology
### Data Processing  
After each measurement along the route, the data sent to the LoRa gateway were stored and transmitted to an MQTT broker server. Received signal strength indicator (RSSI), signal-to-noise ratio (SNR), spreading factor, and GPS position data were downloaded in.tx files and processed in codes using the MATLAB(r) R2023a computational tool. With the data already loaded into MATLAB, we focused on the dataset containing all the necessary information for modeling. RSSI and SNR data are the parameters used to calculate the path loss value based on [29; 37], as outlined in:  
\[PL=\begin{cases}Pt+Gt+Gr-RSSI,&SNR>0\\ Pt+Gt+Gr-(RSSI+SNR),&other\end{cases} \tag{1}\]  
where \(PL\) is the path loss in dB, \(Pt\) is the transmitter power, and \(Gt\) and \(Gr\) are the gains of the transmitting and receiving antennas, respectively. We modeled the data to characterize the uplink channel, by adjusting the classic close-in and floating intercept models. The adjusted parameters include the path loss exponent (\(n\) or \(PL\)), the \(\alpha\) intercept, and the \(\beta\) slope for spreading factors 7, 8, and 9, respectively. We compared fitted models with Okumura-Hata for suburban environments. Additionally, we propose a model for characterizing the vegetation attenuation based on Recommendation ITU-R P-833. Introducing this new model factor, we considered the Friis free space formula and the attenuation model for channel characterization in environments with dense vegetation typical in the Amazon region. We created a MATLAB(r) code to model and adjust all propagation models for the uplink channel.  
Figure 6: Schematic of the route taken by the drone (route in red).
# Collaborative Indoor Positioning by Localization Comparison at an Encounter Position
## 4 Experiments
### Evaluation of Encounter Communication  
The encounter communication was evaluated on two facilities in the test dataset. As shown in Figure 14, two users moved step by step. Then, the distance was measured between the users when an encounter was detected. Ten trials were conducted. The amount of data exchanged at the encounter communication was 500 Bytes.  
Table 5 shows the results of the encounter communication. Although the encounter communication failed once, the proposed method successfully detected encounters at an average distance of 1.9 m. The detected distance in Building No.1 was smaller than that  
Figure 12: Importance of features in the localization comparison model.  
Figure 13: Average output of the localization comparison model.  
for the Complex building. The surrounding walls in the route in Building No.1 are partly made of glass. Thus, the radio waves went through the glass and communication was detected at a close position due to fewer reflected waves.
## 4 Performance Evaluation
### Experimental Results
#### 4.3.3 Disk Utilization Results  
In Figure 7, the x-axis represents time in seconds ranging from 1 to 2000, while the y-axis indicates the disk utilization (%). In Figure 7, we present the simulation up to the 500 s mark, as there are no changes beyond this point, and for reasons of clarity and space. Different strengths and potential weaknesses in managing edge cloud settings are highlighted by the observed patterns in disk consumption across the algorithms, which have a substantial impact on overall system performance and resource utilization efficiency.  
The best-fit algorithm exhibits quick and effective resource allocation, as seen by its early rise and subsequent stabilization at 28% disk use. This low, consistent use shows that the disk is being managed effectively, optimizing resource efficiency and lowering the possibility of overloading any one server. As a result, this technique works well for sustaining steady performance even under increasing workloads.  
On the other hand, by dividing workloads equally among all servers, the worst-fit method, which stabilizes at 50% utilization, demonstrates a balanced approach. Although this guarantees a consistent distribution of resources, it might also imply that no single server is completely optimized, which could result in unrealized potential that could be more effectively employed.  
The power-saving algorithm, after initial stabilization at 41%, indicates its approach of giving low-power-consumption servers priority. Nevertheless, compared to the best-fit algorithm, this results in increased disk consumption, suggesting a trade-off between power efficiency and disk management. This tactic could result in less-than-ideal disk usage, which could affect system performance as disk demand rises.  
The location-based approach shows a substantial degree of variability in disk resource management with its many phases and stabilizing at a high 68% disk utilization. By dynamically allocating jobs based on user location, it substantially minimizes latency, yet the high disk utilization suggests possible inefficiencies. Should the need for disk space increase, the disk could eventually fill up to capacity, which could cause workload problems, slowdowns in performance, or even server breakdowns.  
In conclusion, these patterns imply that whereas certain algorithms enhance particular facets of resource management, they can also entail trade-offs that affect the effectiveness and performance of the system as a whole. Reaching disk capacity, in particular, may result  
Figure 7: Disk utilization per algorithm.  
in catastrophic failures, highlighting the necessity of balanced approaches that take into account all resource dimensions to provide stable and dependable edge cloud systems.