esa-sceva/satcom-chunk-collection huggingface dataset:
# Managing a Smart City Integrated Model through Smart Program Management
## 8 Results
### Project Interdependencies
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**CORRELATION** & Prj **BO1** & Prj **BO2** & Prj **BO3** & Prj **BO4** & Prj **BO5** & Prj **BO6** & Prj **BO7** & Prj **BO9** & \(\sum\limits_{j=1}^{j=8}\)_PI/Prj,j\(\mu\)_0-1 \\ \hline Prj BO1 & 1 & 0 & 1 & 0 & 0 & 1 & 0 & 0 & **2** \\ \hline Prj BO2 & 0 & 1 & 1 & 0 & 1 & 0 & 0 & 0 & **2** \\ \hline Prj BO3 & 1 & 1 & 1 & 0 & 0 & 0 & 1 & 0 & **3** \\ \hline Prj BO4 & 0 & 0 & 0 & 1 & 1 & 1 & 0 & 0 & **2** \\ \hline Prj BO5 & 0 & 1 & 0 & 1 & 1 & 1 & 0 & 0 & **3** \\ \hline Prj BO6 & 1 & 0 & 0 & 1 & 1 & 1 & 0 & 0 & **3** \\ \hline Prj BO7 & 0 & 0 & 1 & 0 & 0 & 0 & 1 & 1 & **2** \\ \hline Prj BO9 & 0 & 0 & 0 & 0 & 0 & 0 & 1 & 1 & **1** \\ \hline \(\sum\limits_{j=1}^{j=8}\)_PI \((Prj\,i,j)-1\)_ & **2** & **2** & **3** & **2** & **3** & **3** & **2** & **1** \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Correlation matrix for Bologna project interdependencies (PI) evaluation. value between the smallest number and the median of PI (first quartile, Q1); the middle value between the largest number; and the median of PI (third quartile, Q3). If we consider Milan, for example, the minimum value of PI was 6, the median was 22, and the first quartile was 14 (there are exactly 25% of PI that are less than the first quartile and exactly 75% of PI that are greater). The third quartile was 31.5 and the maximum (65) was an outlier, so the upper whiskers are drawn at the great value smaller than 1.5 interquartile range (IQR) above the third quartile, which is 55. Bologna and Rome were the only cities with a PI value of 1, but they were also the two Smart cities with less smart projects. Bologna, Rome, Cagliari, and Lecce did not have outliers (i.e., any observations that were more than 1.5 IQR below Q1 or more than 1.5 IQR above Q3 were considered outliers [56]). This reinforces what was previously highlighted in Section 8.2. Bologna was able to develop projects on several layers and to fully exploit the advantages of the layered architecture. Also, Bari and Terni included outliers: two projects with PI = 24 for Bari and five projects for Terni with PI equal to, respectively, 19, 22, 25, 26, and 26. This confirms the existence of redundant projects unable to exploit potential areas of reuse, with loss of economies of scale and scope. After removing the outliers, Bari, Cagliari, and Lecce had a minimum value of PI equal to 4 and a maximum value of 15 out of 25 projects for Bari, 31 out of 32 for Cagliari, and 23 out of 26 for Lecce. Milan and Turin were the two cities with the highest number of smart projects and Turin, despite the removal of outliers, still remained the Smart city with the highest value of project interdependences.
## 1 Introduction
### Design Parameters
As a result, earphone designers must carefully balance various factors when selecting diaphragm materials, including cost, weight, durability, and sound quality. By carefully considering these factors and leveraging advancements in materials science and engineering, designers can create earphones that provide exceptional sound quality and a superior listening experience for users. The three most common types of headphone drivers found in the market are dynamic units or moving coils, balanced armature units, and planar magnetic units [12]. Dynamic units or moving coil drivers are the most common and use a permanent magnet and a voice coil to generate sound. Balanced armature drivers use a smaller, more precise armature and a balanced magnetic field to produce sound. Finally, planar magnetic drivers use a thin film membrane with a conductive layer and an array of magnets to create sound [13]. Each type of driver has its own unique properties and can produce a distinct sound profile. For example, dynamic driver/moving coil drivers are known for their ability to produce deep bass tones, while balanced armature drivers are known for their precision and clarity in the mid- to high-frequency range. Planar magnetic drivers are known for their ability to produce a wide soundstage with exceptional detail and accuracy [12]. Earphone designers must carefully consider the strengths and weaknesses of each type of driver and balance them with other factors such as weight, cost, and overall sound quality when designing earphones. By leveraging advancements in driver technology and materials science, designers can create earphones that provide exceptional sound quality and a superior listening experience for users. In addition to the drivers and the mentioned design parameters, other factors can also affect the sound quality of earphones. For example, the size and shape of the earphone housing can affect the soundstage and the perceived range of frequency response. The ear tips, which are in direct contact with the ear canal, can also affect the sound quality by altering the seal and the amount of outside noise that is blocked. Finally, the cable quality can also affect the sound quality by minimizing electrical interference and signal loss. Nonetheless, the influence of the related factors was found to be relatively small. Hence, due to the consideration of simplifying the problem formulation, the associated factors were omitted in the model so that an accomplishable formulation could be derived, while the variants and expansion will be included in the later development of the research.
# A Review of Femtosecond Laser-Induced Emission Techniques for Combustion and Flow Field Diagnostics
## 2 Femtosecond Two-Photon Laser-Induced Fluorescence (fs-TPLIF)
Femtosecond-TPLIF measurement of Xe was demonstrated in the plasma induced by discharges [49], which might be useful for studying the ignition behavior of flammable gases. In summary, fs-TPLIF has shown great potential for combustion species measurements, owing to its advantages of high multi-photon efficiency and little photolysis interference. More studies are yet to be performed. For example, fs-TPLIF can be further used to measure other combustion intermediates such as atomic C [66; 67] and N [22]. In addition, the technique has not been applied to measure any polyatomic molecule, such as NH\({}_{3}\)[68], which is also of great interest to the combustion community. Furthermore, two-color fs-TPLIF is another strategy that is worth trying. For single-color fs-TPLIF, only one tunable UV laser is needed, but the problem is that its pulse energy is quite limited, while for the two-color strategy, we could use two lasers at two wavelengths. Hence, we have the flexibility to choose at least one laser with strong pulse energy, e.g., from the fourth-harmonic output (200 nm) of an fs Ti:sapphire laser, and then select a tunable UV laser to match the strong laser. In doing so, the excitation efficiency might be improved.
# Vehicle Trajectory Prediction Using Residual Diffusion Model Based on Image Information
## 4 Proposed Approach  
### Overview  
This section describes the network architecture for trajectory prediction, as shown in Figure 2. Our algorithm model includes three main components: encoder, residual denoising generation network and decoder. Encoding is divided into two parts: scene encoding and social encoding. The scene encoder processes static obstacles, lanes, etc., and converts these data into relevant information such as geometric features of the road. The social encoder uses a one-dimensional convolutional network to extract features of vehicle motion states. The target area estimation uses the GAnet [24] method to predict the vehicle destination to improve the accuracy. The trajectory decoder then constructs the predicted paths, combining these estimates with the trajectory constraint module to achieve accurate  
Figure 1: The figure shows the model data preprocessing demonstration. On the left is the conversion method of vehicle data to the vectorized network diagram, including the calculation of the coordinate vector of the surrounding vehicle and the lane line with the selected vehicle as the coordinate point. On the right side is the quantitative data input for predicting vehicle feature information, such as vehicle position and speed information, which is encoded as the input of state matrix \(\mathbf{S}\) in model prediction.  
predictions. We describe the implementation details of our method in the Appendix A at the end of the paper, including training parameters, network structure, training and inference pseudocode.
## 4 Research Methodology
### Feature Selection  
One of the main issues in ML is selecting the appropriate features for the ML algorithm, and the criticality of this has been discussed by many researchers such as [43, 44]. Selecting the right features has the following benefits:  
* Variance (overfitting) is reduced.
* Computational cost and the time for running the algorithm is reduced.
* Enables the ML algorithm to learn faster.  
There are several techniques that can be used for selecting the appropriate and best features and [45, 46] discuss these in detail. For example, two of these techniques are:  
* Filter method--Feature selection is independent of the ML algorithm.
* Wrapper method--A subset of the features are selected and used to train the ML algorithm. Based on the results, features are either removed or added until the best features are determined.  
For this research, the features were studied [47, 48, 49, 50] and based on this, the features were divided into two groups, called Feature set1 and Feature set2, and only the features from Feature set1 were used during this research. Feature set2 contained those features that were not used during this research and were excluded. This was because these features could potentially be related to the characteristics of the network from which the packets were extracted, resulting in the ML algorithm making false correlations. For example, if the benign and malware traffic came from a particular IP address range, the ML algorithm might use the IP address information to make predictions. Table 1 shows the features that were excluded (Feature set2). All the remaining features were included in Feature set1 and were used during this research. These features are: total_fpackets, total_fvolume, total_bpackets, total_bvolume, min_fpktl, mean_fpktl, max_fpktl, std_fpktl, min_bpktl, mean_bpktl, max_bpktl, std_bpktl, sflow_fpackets, sflow_fpytes, sflow_bpackets, sflow_bpytes, fpsh_cnt, bpsh_cnt,  
Figure 2: Methodology for collecting and preparing the data.  
furg_cnt, burg_cnt, total_flhen, total_bhlen, duration, min_active, mean_active, max_active, std_active, min_idle, mean_idle, max_idle and std_idle.
## 3 Data and Methodology
In the analysis of groundwater quality for the period from 1 December 2017 to 30 September 2019, data on chloride and sulfate concentrations of the Zadar were used. In the considered period, chloride concentrations exceeded the MAC values only at the Golubinka well and the Boljkovac water pumping station. Analysis of the chlorine concentrations in groundwater in various parts of that area was conducted to demonstrate this increase due to two possible reasons--the natural conditions of circulation of groundwater and sea in the wider area of their leakage, as well as depletion of water supplies in adverse hydrological conditions. Additionally, increases in chloride content occur during prolonged droughts periods in the form of gradual rise in chloride content or during such prolonged droughts periods as a result of significant precipitation [29].
## 3 Results and Discussion  
### Crack and Failure Geometry  
Figure 6 presents the final failure geometry of the carbon grid-reinforced specimens.  
The specimens with both biaxial warp-knitted and cross-laminate structured carbon grids demonstrated the occurrence of cracks at the center of each specimen. Subsequently, new cracks occurred from the center of the specimen to both ends owing to the applied load. At the center of the specimen, the mortar was crushed and separated.  
In specimens G1, G2, and G3 reinforced with the biaxial warp-knitted structured carbon grids, 11, 17, and 11 cracks occurred, respectively (Table 5 and Figure 7). The  
Figure 5: Test set-up.  
Figure 6: Failure of the specimens reinforced with: (**a**) biaxial warp-knitted; and (**b**) cross-laminate structured carbon grids.  
average crack spaces ranged from 45 to 63 mm, wider than the transverse strand spacings of the carbon grids. Furthermore, Figure 7 indicates that a bond crack between the carbon grid and mortar was observed in the case of specimen G2.  
Conversely, the specimens reinforced with the cross-laminate structured carbon grids had fewer cracks, ranging from four to ten, compared with those observed in the specimens reinforced with the biaxial warp-knitted structured carbon grids. Moreover, the average crack spaces ranged from 81 to 106 mm. The average crack space for specimen J2 was 106 mm, which was more than twice the transverse strand spacing of 50 mm. On the other hand, the average crack spaces of other specimens were smaller than the transverse strand spacing of 100 mm.  
Bond cracks were also observed in specimen J2 (Figure 7); as in specimen G2, the average crack spaces in specimens J2 and G2 were considered to be more than twice the transverse strand spacings because of the poor bond between the carbon grids and mortar.  
By the end of the experiment, no strand fractures were observed in the specimens reinforced with the cross-laminate structural carbon grids. Furthermore, Figure 8 indicates that only a few fibers of the strands were disconnected in specimen J5. However, in the case of specimens reinforced with the biaxial warp-knitted structured carbon grids, the longitudinal strands were fractured in specimens G3 and G1 when the mortar was removed from the center of the specimens after the experiment (Figure 8). Additionally, residual deformation of the carbon grid was observed in specimen G2. In the case of biaxial warp-knitted structured carbon grids, several voids occurred in the cross-section of the strands as they were manufactured using the hand lay-up method (Figure 3a). The strands appeared to be fractured owing to the increase in the applied force and the concentration of this force on the voids.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Specimen** & **Number of Cracks** & **Average Crack Space (mm)** \\ \hline G1 & 11 & 63 \\ G2 & 17 & 45 \\ G3 & 11 & 63 \\ J1 & 7 & 81 \\ J2 & 4 & 106 \\ J3 & 7 & 99 \\ J4 & 8 & 98 \\ J5 & 10 & 88 \\ J6 & 8 & 83 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Crack formation of the specimens.  
Figure 7: Specimen crack spaces.
## 3 Materials and Methods
### Factor Formulation  
Assuming Gaussian measurement and process models, we can write (9) as a nonlinear least-squares problem:  
\[\begin{split}\hat{\mathcal{X}},\hat{\mathcal{Q}}&= \operatorname*{arg\,min}_{\mathcal{X},\mathcal{Q}}-\log P(\mathcal{X}, \mathcal{Q}\mid\mathcal{B},\Theta^{s},\mathcal{I},\mathcal{C})\\ &=\operatorname*{arg\,min}_{\mathcal{X},\mathcal{Q}}\left\{ \sum_{t=0}^{T}\left\|E_{\text{{\it Odom}}}(\tilde{\mathbf{x}}_{t},\mathbf{x} _{t})\right\|_{\Sigma_{o}}^{2}+\right.\\ &\left.\sum_{k=0}^{K}\left\|E_{\text{{\it Box}}}\left(\mathbf{Q}_{ k}^{*},\mathbf{x}_{t_{k}},\tilde{\mathbf{B}}_{k}\right)\right\|_{\Sigma_{b}}^{2}+\right. \\ &\left.\sum_{d=0}^{D}\left\|E_{\text{{\it Label}}}\left(\mathbf{Q}_{ k}^{*},c_{j}\right)\right\|_{\Sigma_{t}}^{2}+\right.\\ &\left.\sum_{j=0}^{T}\left\|E_{\text{{\it Sym}}}\left(\mathbf{Q}_ {j}^{*},\theta_{j}\right)\right\|_{\Sigma_{s}}^{2}\right\},\end{split} \tag{11}\]  
where \(E_{\text{{\it Odom}}},E_{\text{{\it Box}}},B_{\text{{\it Label}}}\) are odometry factors, bounding box measurement factors and semantic prior factors, respectively. The formulation of these factors has been discussed comprehensively in [13]. \(E_{\text{{\it Sym}}}\) is the new symmetric factor and should reflect the error between the symmetry plane of the estimated landmark \(Q\) and its detected symmetry planes \(\{\theta_{j}\}\). However, because of the ambiguity of the object coordinate system, as shown in Figure 6a, the error cannot be simply defined as the subtraction of two angles. Instead, it should relate to the minimum rotation angle required to align the estimated object's x-ory-axes parallel to the measured symmetry plane. We define the error function of the symmetric factor as  
\[E_{\text{{\it Sym}}}=\sin 2(\theta_{O}-\theta_{S}) \tag{12}\]  
With a period of \(\pi/2\), as shown in Figure 6b, the value of the function is 0 when \(\theta_{o}\) is in the same or opposite direction as the x- or y-axes, and takes the maximum value when it is not close to either of the two axes, satisfying the above requirements while having good derivability.
## 3 Critical and Rare Metals in Greece
### Rhenium (Re) and Molybdenum (Mo)  
Rhenium is very resistant against corrosion and is used in high-temperature superalloys for jet engines and industrial gas turbine engines. It improves the properties of tungsten and alloys in X-ray sources and in thermocouples to measure temperatures up to 2200 \(\,\)C. Rhenium is also used in filaments, in chemical catalysts at petroleum industry, in space craft thrusters, in electrical contacts, in coatings, in flash lamps for photography. Radioactive rhenium is used in prevention and treatment of restenosis and liver cancer.  
Molybdenum has various applications in the construction of grade and stainless steel, in chemicals such as lubricants, in the manufacturing of tool and high-speed steels, in the manufacture industry of cast iron and molybdenum metals, as well as in super alloys.  
The world's highest grade molybdenum-rhenium deposit is located in Merlin at the Mount Dore ore prospect, consisting mainly of molybden, with minor chalcopyrite and pyrite, and is hosted within a series of altered carbonaceous metapelites and phyllites [30]. The mineralization exceeds over 1 km in length and is up to 40 m thick, to approximately 550 m from the surface. The current resources average 0.6% Mo and 10 ppm Re.  
Molybdenum occurs mainly in three mineralization styles in Greece [31]: (1) Porphyr Cu-Mo and Mo \(\pm\) Cu (Pagoni Rachi/Kirki, Ktismata/Maronia, Myli/Esymi, Konos/Sapes, Skouries/Chalkidiki, Melitena, Sardes/Limnos, Fakos/Limnos, Stypsi/Lesvos); (2) Reduced intrusion-hosted Mo-W systems (Kimmeria/Xanthi, Pigi/Kilkis, Plakap/Lavrion, Serifos Island); (3) Shear zone related Cu-Au-Bi-Mo (Stanos/Chalkidiki).  
A small molybden production was operated in Pigi in 1940-1944 with variable Mo grade, from 0.3% to 1.3% (rarely up to 10%) with an average of 0.6%-0.7% [14]. There is not any estimation about the current Mo reserves.  
The porphyry Cu-Mo and Mo \(\pm\)-Cu prospects of Pagoni Rachi, Maronia and Melitena, in western Thrace contain molybden with high to ultrahigh Re content (reaching 4.7 wt %), which are the highest concentrations of Re-in-molybdenum ever measured [31; 32; 33]. The microprobe analyses demonstrate that there is a positive correlation between Mo and Re supporting the concept that Re substitutes for Mo in the structure of molybden. Moreover, the very rare mineral rhenius (ReS\({}_{2}\)) is found in the porphyry systems of the Pagoni Rachi and Konos/Sapes prospects [31; 33].  
Rheniite and high-Re molybden in these deposits precipitated under oxidizing conditions and from relatively acid hydrothermal solutions. The extremely high contents of rhenium in molybden at the Greek porphyry prospects could be explained by a rhenium release from the asthenospheric mantle wedge by subduction-related fluids and melts [33]. The Re-enrichment in the western Thrace molybdenites, suggest that this region should be a potential target for future Re- and Mo exploration and exploitation.
# Antennas and Propagation: A Sensor Approach
### Electromagnetic Field Measurements and Remote Sensing Applications  
In Ref. [17], the authors propose an ultra-wide band (UWB) antenna system and a direction-finding (DF) approach based on using energy-based descriptors instead of classical frequency domain parameters. The method can be applied for locating electric discharges in high-voltage power distribution systems through their electromagnetic signature in the radio frequency range.  
A calibration method for high-resolution, hybrid MIMO turntable radars is presented in [18]. A line of small metal spheres is employed as a test pattern in the calibration process in order to measure the position shift caused by undesired antenna effects. The unwanted effects resulting from the near-field antenna response are analyzed, modelled and significantly mitigated, by exploiting the symmetry and response of the MIMO configuration.  
In Ref. [19], the authors show that the distance averaging technique can be applied to reduce the effect of the common mode currents for measuring the field radiated by symmetrical antennas. Two measuring configurations are proposed, depending on the number of symmetry degrees of the antenna under test. A differential approach for extracting the field created by the common mode currents is also developed.  
In Ref. [20], the authors elaborate on how a sensor using modified split ring resonators (SRRs) can be designed, simulated, fabricated and used for advanced investigation and accurate measurements of the complex permittivity of solid dielectrics.  
The work in [21] proposes a near-field to far-field transformation algorithm based on spherical wave expansion for near-field RCS measurements. Each weight in this expansion is calculated by using an iterative least squares QR factorization method. The proposed NFFFT is verified for several types of scatterers.  
This research received no external funding.  
Not applicable.  
The author declares no conflict of interest.
# On Vulnerability of Selected IoT Systems to Radio Jamming--A Proposal of Deployment Practices
## 5 Presentation of the Selected IoT Systems: The Weightless and the Sigfox  
### Systematics of IoT Systems  
To date the IoT systems have achieved technological maturity and developed into two major groups and a few sub-groups within them, as shown in Figure 2. Their common feature consists of providing low-throughput, extremely long rage connectivity to the machine-type communication (MTC) traffic, i.e., one originating from multiple devices deployed across an area much vaster than available with traditional wireless technologies (such as GPRS). They differ though in the way they have originated, i.e., either as new proprietary solutions (LTN in Figure 2) or over the course of evolution of traditional cellular technologies (Cellular IoT, CloT in Figure 2) [15; 16; 17]. The latter, CloT, has spawned three alternative systems, known as Enhanced-Coverage Global System for Mobile Communications for MTC (EC-GSM-MTC), Long-Term Evolution MTC (LTE-MTC) and Narrowband IoT (NB-IoT). The systems of interest in the paper belong to two LTN sub-groups. The first sub-group, represented by Weightless, is Low-Power Wide Area Network (LPWAN), specified in [18; 19; 20] and characterized by bandwidths (BW) of 10-500 kHz and data rates \(R_{b}\) of c.a. 1-100 kb/s. The other sub-group, represented by SigFox, is ultra-narrowband (UNB) [21], embracing systems whose channels bandwidths, compared with the total transmission band, satisfy a relationship of being no greater than 1:100. In SigFox with an uplink BW of 100 Hz, this ratio reaches 1:1920, since the entire band has a width of 192 kHz.