esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Study Area and Past Monitoring Analysis  
### Geological Framework  
The study area is located in NW Italy, on the western shore of the Orta Lake (Figure 1).  
From a geological point of view, the sanctuary is located on a massive granite outcrop known as Granito di Alzo [24]. This unit belongs to the non-metamorphosed, and generally low deformed, granitic masses, related to a late-Hercynian magmatic intrusion (lower Permian). The magmatic unit outcrops along the contact between the lithologies of the \"Serie dei Laghi\" and the Ivrea-Verbano Zone. These granites, commonly known as \"Graniti dei Laghi,\" make a large batholith elongated in the NE-SW direction (Figure 1). The most widespread facies of the Alzo-Roccapietra pluton is a white, medium-grained biotitic granite. The pluton was not significantly tilted after the first intrusion and preserved its original position. The granite bedrock in which sanctuary was built shows excellent geomechanical features Geological strength index (GSI) >70 and high uniaxial compression strength >50 MPa), while the fractured blocks present poor quality [20].  
The Madonna del Sasso sanctuary is located on the top of a cliff at 650 m a.s.l. (Figure 2A) delimited by vertical slopes on three sides (N, E, and S) (Figure 2B,C). The scarps have a height of about 200 m. The cliff is characterized by the presence of different discontinuity sets that could create critical intersections, which are a predisposing factor for the activation of rockslides. The presence of this instability is quite evident in the frontal part of the courtyard, where one of the main discontinuity crosses from SE to NW the entire court.  
The actual aspect of this cliff is also related to a combination of the structural settings and the presence of a quarry along the southern side of the slope that stopped its activity a few decades ago.  
At the base of the cliff, talus and boulders are the result of past quarry activity and rockfalls. According to the critical stability condition and the presence of several elements at risk, at the end of the twentieth century, a protection wall was erected that aimed to reduce the possibility that detached blocks can reach the provincial roads and the nearby buildings (Figure 2D).  
Figure 1: Location and geological settings of Madonna del Sasso site from the geological map of Piemonte retrieved from the WebGIS service of ARPA Piemonte and _Istituto di Geoscienze e Georisorse - Consiglio Nazionale delle Ricerche_ CNR-IGG (Geosciences and Earth Resources Institute of National Research Council of Italy) [25], based on the work of [26].
# Converging Telco-Grade Solutions 5G and beyond to Support Production in Industry 4.0
## 5 The Telco Cloud Enabled 5G Applications
### Telco Cloud vs. IT Cloud  
Table 6 clarifies the main differences between Telco cloud and IT cloud as stated in [183,201,202].  
### Telco Cloud vs. IT Cloud  
Table 6 clarifies the main differences between Telco cloud and IT cloud as stated in [183,201,202].  
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Parameter** & **Telco Cloud** & **IT Cloud** \\ \hline Platform type & Open and scalable platform & May utilize seller restrictive advancements. \\ \hline Terminology & Telco Cloud commonly refers to a Private Cloud deployment within a Telco/ISP environment that hosts Virtual Network Functions (VNFs) of the Telco/ISP Network utilizing NFV techniques & Related to an enterprise workload and is a private cloud deployment. IT Cloud provides cloud based services to render enterprise requirements. \\ \hline Applications & Telecommunication applications & User applications \\ \hline Latency & Very low latency 5 ms & Low latency 1 s \\ \hline Availability & High availability 5–7 nines & High availability 3 nines \\ \hline Throughput & Very High throughput 100 Gbps & High throughput 10 Gbps \\ \hline Related technologies & OSS, VNF, NFV, SDN & Virtualization, IT workload \\ \hline Reliability & Very high & High \\ \hline Performance & Very high performance & Lower performance \\ \hline Arrangement & Distributed Data Centers & Centralized Data Centers \\ \hline Security & Very high & High \\ \hline Oversubscription and CPU Allocations & Ratios are typically 1:1 & Ratios may vary from 8:1 upto 16:1 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Comparison of Telco Cloud and IT Cloud.  
Figure 14: Data Center-proposed types
# Sparse Representation and SVM Diagnosis Method for Inter-Turn Short-Circuit Fault in PMSM
## 4 Experimental Results and Analysis
### Training and Testing Results of SVM  
Mark the extracted features as \(F1\), \(F2\), \(F3\), \(F4\), where \(F1\) means the first feature of current, \(F2\) means the second feature of current, \(F3\) means the first feature of vibration, and \(F4\) means the second feature of vibration. Then the input become \(x_{i}=[F1_{i}F2_{i}F3_{i}F4_{i}]\). The 40 feature vectors obtained above are divided into two sets, where 34 sets are training sets and 6 sets are test sets. The number of normal and fault feature vectors in each set is the same.  
So, the sample points for training SVM are \((x_{i},y_{i})\), and \(y_{i}\) is their corresponding fault type, \(i=1,2,\cdots,34\). Input them into the SVM to get the support vectors and hyperplane. The sample points and support vectors (SV) are shown in Figure 12 where the two features of current act as the coordinate axes.  
Figure 11: Features extracted from the vibration signals (**a**) First feature of vibration; (**b**) Second feature of vibration.  
Figure 12: Sample points and support vectors by \(F1\) and \(F2\).  
The sample points and support vectors (SV) are also shown in Figure 13 where the two features of vibration act as the coordinate axes.  
As can be seen, in the two figures, the sample points of the normal signals are concentrated in the lower left corner, and the sample points of the fault signal are scattered in the upper right corner. After the training is completed, input the 6 test sets to test. The results are shown in Table 2.  
The accuracy of the above test results is 100%, which proves the features extracted by sparse representations can be used for fault diagnosis of PMSM, and the method proposed in this paper is accurate.
## 1 Introduction  
Epireretinal membrane (ERM) is a common retinal disease with a prevalence ranging from 2.2% to 28.9% [1, 2, 3]. Fibocellular proliferation at the vitreoretinal interface, above the internal limiting membrane (ILM), seems to be a key mechanism [4]. ERMs are mainly idiopathic, and are less commonly secondary due to trauma, inflammatory disease, intraocular surgery or retinal detachment [5]. Reduced visual acuity and metamorphosis are the most frequent symptoms [6]. Tractional stress caused by the ERMs may induce changes, such as increased retinal thickness; the formation of lamellar and full-thickness macular defects; and alterations of the outer foveal region [4, 7]. Additionally, other cellular and vascular changes, such as hemodynamic changes in choroidal flow, leakage and disruption from the retinal capillary system, reduced uveal-scleral outflow and breakdown of the retinal pigment epithelium have been reported [8, 9, 10].  
In the last decade, high-resolution spectral-domain coherence tomography (SD-OCT) has emerged as a new non-invasive and reproducible technique for the diagnosis and follow-up treatment of different ocular diseases [11; 12; 13; 14; 15; 16]. To date, this diagnostic tool is considered an essential component of the pre-operative assessment and allows for quantification of structural changes to the macula and specific retinal layers. Additionally, the possibility to investigate such changes and defining new descriptive terms have improved our pathophysiological understanding significantly, identifying possible prognostic factors, able to predict the functional outcomes in macular diseases [1; 17; 18; 19; 20].  
The central bouquet (CB) is a small circular island, less than 100 \(\upmu\)m in diameter and located centrally at the fovea, detectable by OCT [7; 21]. [PERSON] at al. classified tractional abnormalities of the CB and reported a correlation between morphologic progression and the corresponding best corrected visual acuity (BCVA) [7]. Accordingly, they also developed a staging system for describing ERMs and observed that more severe stages of ERMs correlate with higher reduction of visual acuity [4].  
Pars plana vitrectomy (PPV) with ERM and ILM peeling is the gold standard to release tension and restore the normal structure of the retina [22; 23; 24]. However, despite the progress towards a minimally invasive vitreoretinal surgery and high anatomical success rates, the postoperative visual outcomes are variable [25; 26].  
To our knowledge, previous studies have already described irregularities of the outer retinal anatomy in the context ERMs [27; 28]. However, the influences of their surgical removal on specific changes in the CB morphology and correlations with functional outcome have only been investigated in the short-term [29].  
The aim of this study was to evaluate the impacts of different stages of ERM and CB abnormalities on the functional and anatomic outcomes following PPV with ERM and ILM peeling.
## 4 Evaluation
### Comparison with Other Methods
The Pearson correlation between the averages for PESQ and autocorrelation is 0.8857, which is the highest among other evaluation metrics (0.7783 and 0.2237 for average STOI and LSD, respectively). This indicates that enhancement of a harmonic structure in the voiced regions sufficiently contributes to increasing the overall PESQs. The spectra of each reproduced speech signal were visually inspected to verify the effectiveness of the proposed method in terms of maintaining a harmonic structure in the voiced regions. An example of the spectra of the original speech, reproduced speech by the baseline method, and reproduced speech by the proposed method appears in Figure 7. Note that the PD and the HE filter were not adopted in the baseline method, whereas the 3-tab HE filter and an objective function with PD (\(w_{s}\) = 0.25) were adopted in the proposed method. In this example, the selected speech segment corresponded to the typical voiced region in which a harmonic structure was apparent, as shown at the top of Figure 7. Compared with the baseline method (\(w_{s}\) = 0.0 and \(N_{k}\) = 0), the proposed method revealed a clearer harmonic structure. Such a pattern was evident in the relatively low frequency regions (\(\leq\)1.5 kHz in this example), because the human auditory system is most sensitive to signals in the vicinity of 1 kHz, and the DNN was trained to further reduce errors within this band. In the baseline method, however, the human auditory system and HE were not taken into consideration in training the DNN, which resulted in a very weak harmonic structure even for the voiced regions. Also, the amplitudes of the low band harmonics \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method** & **Avg. PESQ** & **Avg. STOI** & **Avg. LSD** & **Avg. R** \\ \hline Linear regression (LR) & 1.515 & 0.424 & 9.700 & 0.114 \\ Multivariate linear regression (MLR) & 1.551 & 0.469 & 9.105 & 0.135 \\ Gaussian mixture model (GMM) & 1.535 & 0.472 & 9.484 & 0.109 \\ Recurrent neural networks (RNN) & 1.648 & 0.538 & 8.306 & 0.133 \\ Long short term memory (LSTM) & 1.684 & 0.556 & 8.063 & 0.159 \\ Multi-layer perceptron (MLP) & 1.786 & 0.551 & 9.300 & 0.160 \\ Multi-layer perceptron (MLP) with PD & 1.999 & 0.553 & 9.503 & 0.211 \\ Multi-layer perceptron (MLP) with PD + HE & 2.108 & 0.567 & 9.590 & 0.402 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Performance comparison of the test set for the different estimation methods. (\(\leq\)800 Hz in this example) were excessively higher than that of other harmonics. This has been a major cause of the total noise that is often perceived in reproduced speech. In conclusion, the problems associated with a loss of harmonic structure that are frequently encountered when using conventional SSI methods were partially alleviated by adopting PD and HE. Problems persisted with the proposed method, however, where the amplitude of each harmonic was either over- or under-emphasized.
## 1 Introduction
As a result, how to establish a tradeoff among detection probability, false-alarm probability and available throughput deserves further discussion. In the literature, three kinds of sensing strategies were explored. In [28], we can conclude that the detection performance is optimal and the false-alarm probability is in the maximum fault tolerance range of CR, when the detection probability is maximum. The CR system can be optimized when the system has the maximum throughput and the detection probability is greater than the minimum requirement of CR [29]. Evidently, [28] tended to improve the detection probability at the expense of false-alarm probability. Simultaneously, [29] sacrificed detection probability for a higher throughput. In [30], the authors insisted that the system performance was optimal when the sum of the false-alarm probability and miss probability was at the lowest level. Due to the negative correlations between false-alarm probability and available throughput, [30] provided a tradeoff among detection probability, false-alarm probability and available throughput. Motivated by the reasons mentioned above, an improved sensing strategy is proposed in this paper, giving a better tradeoff among the detection probability, false-alarm probability and available throughput. The main contribution of this paper is concluded as follows:  
1. We analyze the advantages and disadvantages of three classical sensing strategies and demonstrate the superiority of the proposed strategy. 2. We emphatically compare the proposed sensing strategy with the one proposed in [30] and reach a conclusion that the proposed strategy outperforms the one in [30]. 3. We conduct simulation experiments respectively in the AWGN, Rayleigh and Rician channel to validate our conclusion proposed in this paper. Note that the proposed strategy greatly differs from energy harvesting (EH)-based SS [31]. To the best knowledge of the author, EH-based SS is devoted to collect enough energy from the environment to ensure that sensor nodes could work stably. As for the proposed strategy in this paper, a better tradeoff among detection probability, false-alarm probability and available throughput is considered. The essence of the proposed scheme consists in providing a sensing strategy with PU protection considering the communication capacity of cognitive users. The rest of this paper is organized as follows. Section 2 reviews the basic work of SS. The main contribution of this paper is shown in Section 3, which contains all of the theoretical derivations. Simulation experiments and analysis results are reported in Section 4. Finally, Section 5 concludes this paper.
## 3 Model Development  
### Problem Description  
In the summer of 2022, a food delivery pilot service using UAVs started at Mangsang Beach in the East Sea of Korea. This beach is inconvenient for tourists who use the beach because the shopping area is several kilometers apart from the beach. Therefore, it starts to connect tourists from shopping areas to beaches through UAVs-based food delivery services. Although there were occasional errors because it was a pilot service, tourists were amazed and enjoyed the advanced delivery system using UAVs [30].  
In this study, it is tried to develop an optimal algorithm of the integrated operation for the last-mile delivery service using both UGVs and UAVs in tourist destinations with the objective of maximizing the satisfaction of tourists by reflecting the situation of providing pilot delivery service using UGVs or UAVs for tourists. To understand the problem situation easily, a hypothetical beach as shown in Figure 2 is introduced as a potential tourist destination. Since last mile delivery is considered in this study, it is assumed that goods ordered by tourists on the beach arrive at the depot first from the outside area and then are delivered using UGVs and UAVs. In addition, a certain number of UGVs and UAVs are prepared in the depot, and in the case of UGVs, there are areas that are inaccessible due to topographical reasons (the nodes represented by gray squares in Figure 2), therefore, delivery service is only possible with UAVs in these areas. However, it is assumed that UAVs move in two cases: case 1 where the UAVs move freely in the sky to the target node, and case 2 where the UAVs move outline of the beach to prevent damage from an accidental falling of either the transported goods or the UAV itself. In case 2, UAVs can only provide delivery service to some nodes along the gray path in Figure 2. The goods arriving at the depot are collected for a certain period and delivered to the destination node through UGVs and UAVs, and during that period, UGVs and UAVs can visit the depot multiple times and deliver multiple times. The goods requested for delivery can be broadly classified into food and general item, and each delivery order has a certain weight and volume. Because UGVs and UAVs have capacity limitations in terms of weight and volume, single delivery or multiple delivery is possible, taking into account the weight and volume of each delivery order. Since this integrated delivery system is operated for fun and enjoyment for tourists in a certain tourist destination, it is assumed that customer satisfaction varies depending on the means of delivery (UGVs or UAVs) and decreases depending on the delivery elapsed time. Lastly, UGVs providing these services generally have sufficient battery capacity to operate at period of time, and UAVs are operated in the form of battery replacement, maintenance time is assumed to be neglected in this study.
## 3 Binocular Camera Ranging Principles  
A binocular camera simulates the human eye to imagine a single object from two different angles through binocular vision and calculates the depth of the object via the angular difference [17]. The schematic diagram of the ranging principle of the parallax method is shown in Figure 3.  
\(Q_{R}\) and \(Q_{T}\) are the optical centers of the two binocular cameras, \(P\) is the actual object to be measured, and \(P_{1}\) and \(P_{2}\) are the imaging points (mirror images) of the object to be measured by the sensors of the two cameras. The distance _dis_ between \(P_{1}\) and \(P_{2}\) can be expressed as  
\[dis=B-(X_{R}-X_{T}) \tag{6}\]  
According to the similarity theorem of triangles,  
\[\frac{B-(X_{R}-X_{T})}{B}=\frac{Z-f}{Z} \tag{7}\]  
Figure 3: Binocular camera ranging schematic.  
Figure 2: FMCW signal schematic.  
The vertical distance of the object to be measured from the camera baseline, that is, the depth information \(Z\) -, can be obtained as follows:  
\[Z=\frac{fB}{X_{R}-X_{T}} \tag{8}\]  
where \(f\) is the camera focal length.
# Identifying Misbehaving Greedy Nodes in IoT Networks
## 4 Detection of Selfish Nodes  
As mentioned in the introduction, we have chosen to analyse the most relevant parameters, i.e., those influenced by the behaviour of the greedy node. The number of packets sent parameter is one of the most relevant parameters since the greedy node sends more packets than a normal node. Next, we analyse the number of packets received by each node, because if the greedy node monopolizes the channel partially or totally, it will prevent other legitimate nodes from sending their packets. As a result, it will receive fewer packets than the others. The number of collisions caused by each node is also a parameter to be considered, since the greedy node falsifies its listening channel parameters (number and duration of CCA). It will therefore observe less time to check the channel. Hence, it will cause more collisions. The radio status settings are indicative of greedy behaviour, since they inform about the radio transmission rate (radio Tx) and the duration when the radio is on to either send or receive. Finally, the energy consumption parameters (power, transmit power) and transmit duty cycle are also important to be considered since the greedy node  
Figure 1: The difference between CSMA/CA algorithm and greedy node algorithm.  
Finally, the last step is to determine if a node is legitimate or greedy. A given node is considered greedy if its eight parameters belong to the so-called abnormal values (i.e., greater than or less than the thresholds). In other words, the greedy node must have sent more packets than the threshold Th\({}_{1}\), and the number of collisions caused by the greedy node must exceed the threshold Th\({}_{2}\), and the number of packets received by this node must be less than Th\({}_{3}\), and the Transmit power, Transmit duty cycle, Power, Radio on (%), and Radio Tx (%) parameters must be respectively upper than their appropriate thresholds: Th\({}_{4}\), Th\({}_{5}\), Th\({}_{6}\), Th\({}_{7}\), and Th\({}_{8}\).
###### Abstract  
The paper reports the removal of phosphorus and natural organic matter (NOM) from surface water by dynamic membrane (DM) adsorber. DM filter builds up as a layer of particles deposited via permeation through the membrane's surface. This study reports the application of Heated Aluminium Oxide Particles (HAOPS) as a dynamic membrane adsorber. Filtration experiments were conducted with surface water and batch tests were carried out with synthetic water. The efficiency of phosphorus removal along with the efficiency of organics (represented as UV-254) removal was evaluated. Additionally, the impact of HAOPS surface loading on the changes of transmembrane pressure (TMP), the kinetics, isotherm modeling of the adsorption and the effect of the pH, the effect of ionic strength, the effect of coexisting organic matter on phosphorus removal efficiency were studied. In the case of phosphorus removal in batch adsorption experiments, its sorption kinetic and isotherm data were analyzed using pseudo-first- and pseudo-second-order models and Langmuir and Freundlich models, respectively. The results indicated that Langmuir adsorption isotherm fits the experimental data best (0.9894). The kinetics of phosphorus adsorption on HAOPS was best described by the pseudo-second-order model and the best removal was achieved at the pH 6-7 (96.65% on average). An increase in ionic strength did not impact the efficiency of phosphorus removal significantly. The outcome of this study highlights HAOPS efficiency in NOM removal up to 92% in filtration experiments. In the case of phosphorous, removal efficiency varied. For the highest HAOPS dose, the degree of phosphorus removal ranged up to 93 \(\pm\) 5%. The same removal efficiency was observed for the lowest dose while for 17 g/m\({}^{2}\) of HAOPS the fluctuations were larger and varied from 64.7% to 92%. The results demonstrate that HAOPS could potentially be applied in the removal of phosphorus from surface water, especially when there is a high concentration of phosphorus in the water. However, this process requires further research and optimization of its parameters.  
phosphorus removal; coating layer; pre-deposited dynamic membrane; HAOPS +
Footnote †: journal: Applied Sci. 2021, 11, 7384. [[https://doi.org/10.3390/app11167384](https://doi.org/10.3390/app11167384)]([https://doi.org/10.3390/app11167384](https://doi.org/10.3390/app11167384))