esa-sceva/satcom-chunk-collection huggingface dataset:
# Error Distribution Pattern Analysis of Mobile Laser Scanners for Precise As-Built BIM Generation
## 5 Discussion
### Discussions on the Impact of Downsampling Methods on Error Pattern  
The proposed analysis method adopted random downsampling for each plane-level point cloud. As presented in Section 4.2, sensitivity analysis confirmed that random downsampling preserves the error distribution patterns inherent in the raw point clouds. Therefore, the error patterns considered to reliably represent those of the original point clouds.  
Meanwhile, applications that utilize point clouds in Scan-to-BIM contexts commonly adopt various downsampling methods, such as voxel-based and space-based downsampling, rather than relying solely on random downsampling. Accordingly, it is necessary to discuss the potential risk that the error patterns derived through random downsampling may be altered or disregarded when other downsampling methods are applied.  
Voxel-based downsampling methods are robust approaches that generate fixed size of voxels and select one point for each voxel. The error pattern investigated in this study is not considered during voxel generation. However, the main concern is how to select one point from each voxel. For example, methods that regenerate geometrical center point for each voxel have high potential to disregard the error pattern. By contrast, strategies that pick either a random point or the densest point within each voxel are more likely to preserve the inherent error pattern in point clouds. The same argument applies to octree-based downsampling methods.  
Therefore, the findings of this study are expected to be highly applicable to a wide range of density-aware downsampling methods, which account for local point distributions. While these results may be less suitable for traditional schemes that uniformly regenerate geometrical central points within voxels, they nonetheless offer valuable insights for advanced sampling strategies. Further systematic evaluation is needed to verify how robust error patterns are preserved across various downsampling strategies.
## 2 Infrastructure, Instrumentation and Calibration Methods
### Calibration and Validation Methods
#### 2.2.2 Transponder Cal/Val  
The sea-surface calibration method compares a derived geophysical parameter (sea-surface height or sea-surface anomaly) at a close-by location with ocean truth measurements regularly made on the coast. Nonetheless, the primary measurement of radar altimeter is the propagation time (converted to range if multiplied by speed of light) of a signal pulse from the altimeter antenna to the Earth's surface and back to the satellite. A transponder provides absolute calibration of these direct and fundamental range observations made by the altimeter if the location of the transponder and its internal path delay are precisely and accurately known.  
An active microwave transponder receives the altimeter's signal when a satellite flies over it (which takes about 4 s), amplifies it with minimum distortion and sends it back to the satellite where it is recorded. At first, the received signal is distinctly separated from other surrounding reflections (clutter) of the ground surface as it had been powerfully amplified by the transponder. Then, at explicitly the same time, the geocentric positions of the altimeter in orbit and the transponder on the ground are determined. The theoretical distance between these two instruments in space is subsequently computed. Finally, after extensive processing, the observed range by the altimeter is compared against the theoretical range of the transmitting satellite point in orbit and the center of the transponder. During the overpass period, a time series of the differences between the observed range and the theoretical range provides an estimate of the altimeter range, if short time scale atmospheric components of the troposphere are sufficiently known.  
The PFAC has been providing transponder calibration services for Jason-2, Jason-3, CryoSat-2, Sentinel-3A, and Sentinel-3B from September 2015 as part of the ESA FRM4 ALT project. After each calibration, the echo of the transponder response is recorded by the altimeter and two recent such examples are shown in Figure 2. Transponder calibrations presuppose that the Agency operating the satellite has programmed the altimeter to observe within the measuring window of the CDN1 Cal/Val site on the mountains at the elevation of 1050 m.
## 4 The Effect of RF Exposure _in Vivo_: New Insights on Biosystem Functions
### Can We Trust Wi-Fi(delity)?  
Recently, Wi-Fi exposure (1 h per day, for 36 days) effect was measured in reproductive organs of adults and embryonic development of offspring. In this case, although thoroughly investigated, both morphologically and molecularly, no adverse effect was noted in either the pups or the reproductive functions of parents [86, 87]. [PERSON] _et al._[88] found that whole-body exposure to Wi-Fi (2450 MHz 4 W/kg) during the entire pregnancy of C57 BL/6 mice did not interfere with the number of newborn or lymphocyte B maturation in the offspring thymus. Moreover, T-cells isolated from pups showed no difference in proliferation with respect to the placebo animals. The complexity of the picture does not allow for the underestimation of possibly undiscovered adverse effects on developing biological systems [89], and these data must be evaluated in light of involuntary EMF environmental pollution exposure to children.
###### Abstract  
Structural Health Monitoring (SHM) techniques are crucial for evaluating the condition of structures, enabling early maintenance interventions, and monitoring factors that could compromise structural integrity. Modal analysis studies the dynamic response of structures when subjected to vibrations, evaluating natural frequencies and vibration modes. This study focuses on detecting and comparing the natural frequencies of a 3-DoF structure under various excitation scenarios, including ambient vibration (in healthy and damaged conditions), two types of transient excitation, and three harmonic excitation variations. Signal processing techniques, specifically Power Spectral Density (PSD) and Continuous Wavelet Transform (CWT), were employed. Each method provides valuable insights into frequency and time-frequency domain analysis. Under ambient vibration excitation, the damaged condition exhibits spectral differences in amplitude and frequency compared to the undamaged state. For the transient excitations, the scalogram images reveal localized energetic differences in frequency components over time, whereas PSD alone cannot observe these behaviors. For the harmonic excitations, PSD provides higher spectral resolution, while CWT adds insight into temporal energy evolution near resonance bands. This study discusses how these analyses provide sensitive features for damage detection applications, as well as the influence of different excitation types on the natural frequencies of the structure.  
a Article
_Polymers_**2011**, \(3\), 1006-1028; doi:10.3390/polym3031006  
_Review_  
Exploit the Bandwidth Capacities of the Perfluorinated Graded Index Polymer Optical Fiber for Multi-Services Distribution  
**[PERSON] 1,2,*g, [PERSON] 1,2, [PERSON] 1, [PERSON] 1,2 and [PERSON] 1,2**  
1 Institut d'Electronique, de Micro dectronique et de Nanotechnologie (IEMN), CNRS UMR 8520--Universite de Lille 1 Sciences et Technologies, BP 60069, 59652 Villeneuve d'Ascq cedex, France; E-Mails: [EMAIL_ADDRESS] (C.L.); [PERSON].-P.V.); [EMAIL_ADDRESS] (N.R.); [EMAIL_ADDRESS] (P.A.R.)  
2 Institut de Recherche sur les Composants logiciels et mat riels pour l'Information et la Communication Avanc & (IRCICA), CNRS USR 3380--Universite eLille 1 Sciences et Technologies, 50 avenue Halley, 59650 Villeneuve d'Ascq, France  
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS]; Tel.: +33-320-197-897; Fax: +33-320-717-042.  
_Received: 12 May 2011; in revised form: 15 June 2011 / Accepted: 27 June 2011 / Published: 29 June 2011_  
**Abstract:** The study reported here deals with the exploitation of perfluorinated graded index polymer optical fiber bandwidth to add further services in a home/office network. The fiber properties are exhibited in order to check if perfluorinated graded index plastic optical fiber (PFGI-POF) is suitable to support a multiplexing transmission. According to the high bandwidth length of plastic fibers, both at 850 nm and 1,300 nm, the extension of the classical baseband existing network is proposed to achieve a dual concept, allowing the indoor coverage of wireless signals transmitted using the Radio over Fiber technology. The simultaneous transmission of a 10 GbE signal and a wireless signal is done respectively at 850 nm and 1,300 nm on a single plastic fiber using wavelength division multiplexing commercially available devices. The penalties have been evaluated both in digital (Bit Error Rate measurement) and radiofrequency (Error Vector Magnitude measurement) domains.  
**Keywords:** polymer optical fiber; 10 GbE; Radio over Fiber
## 2 Materials and Methods
### Sorbent Preparation and Characterization  
Titanium dioxide (TiO\({}_{2}\)) was supplied by Evonik (Aeroxide(r), TiO\({}_{2}\) P25 with a crystalline content of 75% anatase and 25% rutile), and the multi-walled carbon nanotubes (CNT) were obtained from Chengdu Organic Chem. Co. (outside diameter from 5 nm to 30 nm, purity \(>\) 95% and length from 10 \(\upmu\)m to 30 \(\upmu\)m). The suspension was prepared by weighing 0.01 g TiO\({}_{2}\) in 10 mL of pharmaceutical aqueous solution. Before immobilization of the catalyst to the support, glass fiber (GF) meshes were cut and adapted to the dimensions of the glass vessels with an average area of 10.17 cm\({}^{2}\). The procedure for sol-gel preparation and characterization of two types of immobilized photocatalysts (TiO\({}_{2}\)-GF and TiO\({}_{2}\)/CNT-GF) on a round-cut glass fiber mesh was the same as described in previously published papers [28; 29]. The average weight of the immobilized TiO\({}_{2}\) film was 0.0037 g/cm\({}^{2}\) and 0.072 g/cm\({}^{2}\) for TiO\({}_{2}\)/CNT, respectively. Adding such a mesh to a drug volume of 10 mL yielded an average concentration of 3.76 g/L. Analogous to the above calculations, the concentration of TiO\({}_{2}\)/CNT-GF mesh was 73.2 g/L, higher than that of TiO\({}_{2}\)-GF at the beginning, so it is expected that sorption will be higher with this type of catalyst.  
In order to get insight in sorbent, clean GF mesh, pure TiO\({}_{2}\), mesh with TiO\({}_{2}\)-GF and TiO\({}_{2}\)/CNT-GF were recorded by a scanning electron microscope (SEM-QUANTA FEG-250, Zaragoza, Spain) operated at 20 kV. Figure 1 shows SEM images and by comparing the surfaces of TiO\({}_{2}\) (b) and TiO\({}_{2}\) after binding to glass fibers (c), it can be seen that agglomeration has occurred. Furthermore, TiO\({}_{2}\)/CNT-GF (d) bonds noticeably produce even larger agglomerates. As it is well known, (I) the surface plays an important role in the sorption process, and (II) the agglomeration reduces the surface area. These are important facts in this paper.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**PhAc** & **IMT** & **CRZ** \\ \hline Formula & C2\({}_{2}\)H3\({}_{1}\)N\({}_{7}\)O & C2\({}_{1}\)H\({}_{2}\)Cl\({}_{2}\)FN\({}_{5}\)O \\ CAS & 152459-95-5 & 877399-52-5 \\ Mr & 493.6 & 450.1 \\ pK\({}_{a}\) & 8.07; 37.3; 2.56; 1.52 & 5.6; 9.4 \\ \(\log K_{ow}\) & 2.89 & / \\ \hline \hline \end{tabular}
\end{table}
Table 1: Physicochemical properties of pharmaceuticals [14; 26; 27].
# Integration of Multi-Branch GCNs Enhancing Aspect Sentiment Triplet Extraction
## 4 Experiments and Discussion
### Datasets  
In this paper, extensive experiments are conducted on four benchmarks, namely Laptop14, Restaurant14, Restaurant15, and Restaurant16, which are public and available for ASTE tasks. Furthermore, these four datasets are all collected from the SemEval ABSA challenges [53; 54; 55]. It's worth noting that these four datasets are revised by [PERSON] et al. [56] and [PERSON] et al. [22] for ASTE tasks, respectively, which are denoted as \(\mathcal{V}_{1}\) and \(\mathcal{V}_{2}\) in this paper. Moreover, the statistics for these two versions of datasets are shown in Table 2.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Datasets**} & \multicolumn{3}{c}{**Laptop14**} & \multicolumn{3}{c}{**Restaurant14**} & \multicolumn{3}{c}{**Restaurant15**} & \multicolumn{3}{c}{**Restaurant16**} \\ \cline{3-10}  & & **\#S** & **\#T** & **\#S** & **\#T** & **\#S** & **\#T** & **\#S** & **\#T** \\ \hline \multirow{3}{*}{\(\mathcal{V}_{1}\)} & train & 899 & 1452 & 1259 & 2356 & 603 & 1038 & 863 & 1421 \\  & dev & 225 & 383 & 315 & 580 & 151 & 239 & 216 & 348 \\  & test & 332 & 547 & 493 & 1008 & 325 & 493 & 328 & 525 \\ \hline \multirow{3}{*}{\(\mathcal{V}_{2}\)} & train & 906 & 1460 & 1266 & 2338 & 605 & 1013 & 857 & 1394 \\  & dev & 219 & 346 & 310 & 577 & 148 & 249 & 210 & 339 \\ \cline{1-1}  & test & 328 & 543 & 492 & 994 & 322 & 485 & 326 & 514 \\ \hline \hline \end{tabular} Note: #S denotes the number of sentences; #T means the number of triplets contained in the datasets.  
\end{table}
Table 2: Statistics of two groups of experiment datasets.
# Development of Indirect Sandwich ELLA for Detection of Insets in Food
## 3 Results  
### Working Dilution of Lectin and Chitosan Solubility pH  
Chitin is a non-soluble natural biopolymer. The solubility of chitosan depends on the pH. This study tested solvents with a pH of 2.4, 7.4, and 9.4. The solubility of chitosan in water is influenced by the hydrodynamic behavior of chitosan macromolecules. At pH 3, chitosan adopts a rigid rod conformation. When the pH exceeds 6, deprotonation of the amino groups begins, leading to a loss of charge on the chitosan macromolecules and the initiation of precipitation [29]. Chitosan's solubility is affected by several factors and can be modified by adding substances such as urea, acetate, PBS, or bicarbonate buffer. These additives adjust the solubility of chitosan across a pH range from 4.0 to 10 [30]. The absorbance value was higher for pH 7.4, independent from chitosan concentration. The lowest was for pH 2.4 (Figure 1).  
In this study, an indirect sandwich enzyme-linked lectin sorbent assay (ELLA) was used to confirm the chitosan content in food products. In brief, purified WGA and LEL lectin were coated onto wells at dilutions of 1:1000 and 1:10,000. The absorbance values at concentrations of 10 mg/mL were all greater than 1.0 at a dilution of 1:000 at pH 7.4, confirming successful labeling and suitability for use as a detection antibody. Due to the lower absorbance value obtained at a 1:10,000 dilution, the lectin dilution was reduced, and dilution ratios were adjusted to 1:1000 for further testing.
# Network Tower Sharing Analysis in Greece: A Structure-Conduct-Performance Approach
## 3 Results  
### Basic Conditions  
The Greek telecommunications market has undergone substantial transformations over the past three decades [30]. Initially operating as a state-owned monopoly through the Hellenic Telecommunications Organisation S.A--OTE in the fixed telephony sector--the market transitioned through phases of privatization and later liberalization [31]. In addition, mobile telephony began in 1992 with two private companies, namely Panafon and Telestet. This was followed by the launch of a third player, Cosnote, entering the market in 1996, owned by the former state-owned monopoly (OTE). Thereafter, the number of telecom operators grew rapidly, reaching fourteen (14) and eleven (11) fixed operators in 2008 and 2011, respectively. In parallel, the number of mobile operators remained steady at three (3) during this time.  
In the following years, consecutive waves of economic crisis affected the Greek economy, resulting in a decline of market demand and supply [32]. By the end of 2022, the number of fixed telephony and broadband operators was four (4), while the number of mobile network operators was three (3) (i.e., Cosnote, Vodafone, and Nova). It should also be noted that although Greece has three MNOs, there are just two mobile network infrastructures in Greece, since Vodafone and Nova have been sharing their mobile network infrastructure for more than a decade. Additionally, Greece currently has no active mobile virtual network operator (MVNO). In contrast, several EU peer group countries, including Germany, Italy, the Netherlands, and Spain, have mobile virtual network operators (MVNOs) holding 10% or more of the mobile subscription market share. This suggests that in these markets, competition extends beyond just the infrastructure-based MNOs, enhancing market dynamics.  
The COVID-19 pandemic crisis decreased Greek electronic communications' turnover to 4.8 billion euros (3.4% decline) in 2020, while the number of employees increased by 1.6% compared to 2019, reaching 14.6 thousand people. Even though the consumer price index (CPI) during 2020 reduced by almost 1%, the communications weight coefficient in the total household expenditure increased to 44.20 compared to 42.41 in 2019, revealing an increased importance when calculating CPI. Finally, sector's share to Greece's gross domestic product (GDP) in 2020 raised up to 2.9%, while it is worth mentioning that even in periods where the weighted average GDP reduced (e.g., \(-\)2.2% for the period 2011-2020), the sector's turnover still increased (e.g., 3.4% for the same period).
# End-to-End Online Video Stitching and Stabilization Method Based on Unsupervised Deep Learning
## 2 Methodology
### Loss Function  
The proposed unsupervised end-to-end method implements joint training across all modules, with a loss function designed to simultaneously optimize camera trajectory estimation, stitching motion estimation, and trajectory smoothing.
## 3 Results and Discussion
### Instance-Level Feature Importance Analysis
#### 3.2.1 Local Interpretation  
An insecure OC/instance and a secure one are analyzed sequentially. The main parameters of these two OCs are shown in Table 1. The background data for interpretation are summarized as 20 samples, using the k-means algorithm to reduce the interpretation time. Figure 5 presents the interpretation results.  
In the insecure OC/instance, the top six features with the highest Shapley value are \(P_{G7}\), \(P_{G4}\), \(P_{AC29}\), \(P_{AC42}\), \(P_{AC43}\), and \(P_{AC44}\). The positive and negative of the numerals  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Operation Condition (OC)** & \(P_{G1}\) & \(P_{G2}\) & \(P_{G3}\) & \(P_{G4}\) & \(P_{G5}\) & \(P_{G6}\) & \(P_{G7}\) & \(P_{G8}\) \\ \hline _Insecure_ & \(5.78\) & \(5.30\) & \(3.23\) & \(1.84\) & \(4.59\) & \(0.00\) & \(2.67\) & \(3.16\) \\ _Secure_ & \(5.50\) & \(4.82\) & \(3.54\) & \(1.77\) & \(4.41\) & \(-0.01\) & \(2.05\) & \(2.57\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Main parameters of an insecure OC and a secure OC in the CEPRI36 system.  
Figure 4: Impurity-based M-FIA for the DSA-DL model.  
Figure 5: Local interpretation for one insecure OC and one secure OC from the perspective of feature importance. Note that the numerals beside bars in (**a,b**) are Shapley values of corresponding features, and the numerals under the bars in (**c,d**) are feature values in units of p.u.  
indicate a category of secure and insecure, respectively. According to (3), the security probability decreases from a primary value of 0.51 to the final value of 0.00 with every feature's contribution, causing the TSA-ML model to predict the OC as insecure. Among the features, \(P_{G4}\) has the highest Shapley value in class secure, which means it has the most influence on secure prediction. Other features influence insecure prediction, in which \(P_{G7}\) influences most. \(G7\) and \(G4\) are determined as the critical generators in this insecure OC. In the CEPRI36 system, the pre-defined fault line, \(AC29\), is located \(G4\) and \(G7\). These two generators are easily influenced by the fault because they would receive the sheer change of power and voltage once the fault occurs. Thus, it is reasonable to indicate that the active power of these two generators casts the most influence on prediction under such fault. Further, \(AC42\), \(AC43\), and \(AC44\) are determined as the critical AC lines in the insecure OC, to which operators should also pay more attention.  
As for the secure OC/instance, the top six features with the highest Shapley value are \(P_{G8}\), \(P_{AC42}\), \(P_{AC43}\), \(P_{AC44}\), \(P_{G7}\), and \(Q_{AC42}\). These features support secure prediction and contribute to pushing the security probability from the primary value of 0.51 to the final value of 1.00, identifying the OC as secure.  
Through SHAP interpretation, it is easy to figure out the feature importance and detect the critical units for any specific OC/instance.
# FF-MR: A DoH-Encrypted DNS Covert Channel Detection Method Based on Feature Fusion
## 4 Method Design
### Data Preprocessing
#### 4.2.2 Session Statistical Features Extraction  
We extract a total of 29 sessions statistical features of five categories, as shown in Table 2. The session duration, number of bytes, packet length, packet time, and request/response time difference are counted. We calculate the mean, median, mode, variance, standard deviation, coefficient of variation, skew from median, and skew from mode for three features, except session duration and number of bytes. At the same time, the rate of session bytes sent and received are calculated because the above five categories of features characterize the characteristics of DoH-encrypted DNS covert channel traffic. For example, DoH-encrypted DNS covert channels contain TCP traffic with covert transmission, which requires more data to be sent, so the values of session duration, number of bytes, and packet length are larger. Compared with the normal DoH and HTTPS traffic, DOH-encrypted DNS covert channel traffic usually has a lower cache hit rate, which leads to a higherlatency, a higher frequency of sending packets, and a larger time difference between request and response.  
After experimental verification, these features can better represent the difference between the DoH-encrypted DNS covert channel and normal DoH and HTTPS traffic. Because of the different magnitudes, numerical values vary greatly in the statistical features, and it is therefore necessary to be standardized to maintain numerical sensitivity. The standardized statistical features can be expressed as:  
\[\mathbf{S}_{i}=\left[s_{1}^{i},s_{2}^{i},\ldots,s_{k}^{i},\ldots,s_{29}^{i}\right] \tag{11}\]  
where \(\mathbf{S}_{i}\) is the statistical feature vector of the \(i\)th session, and \(s_{k}^{i}\) is the \(k\)th statistical feature value of the \(i\)th session.  
We have improved the statistical feature extraction tool DoHMeter [38]. The original DoHMeter tool extracts statistical features in the unit of time-divided streams, and the improved DoHMeter extracts statistical features in session (bidirectional stream) units. Compared with the standard tool, the features extracted by the improved DoHMeter are more complete, sufficient, and more effective for detection using experimental verification.