esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### TEM/EDS/SAED Characterisation  
In both samples (M-17 and M-12), at lower magnification, the TEM images showed mainly the residues of the filter, corpuscles of spherical morphology (Figure 11a), with also sporadic residues of the pre-existing phases, such as chrysotile, dolomite, magnesite and talc (Figure 11b). We mainly focused on the identification and characterisation of the asbestos phases, i.e., chrysotile. The longest fibres reached dimensions of more than 2 \(\upmu\)m in length, with outer and inner diameters being, on average, 50 and 10 nm, respectively. This size is beyond the range of respirable fibres, defined by WHO [17] as fibres having width \(\leq\) 3 \(\upmu\)m, length \(\geq\) 5 \(\upmu\)m and length/width ratio \(\geq\) 3:1. However, the aerodynamics of this motion are caused mostly by the fibres' width [18]. A more accurate size evaluation would require thousands of measurements under SEM and TEM. Unfortunately, chrysotile fibres are often curled up in polypropylene filter residues, and the fibres' sizes may be underestimated.  
At higher magnification, bundles of chrysotile could be observed, in which the individual fibres showed the classic tube shape (Figure 12a,c). All the observed chrysotile fibres showed a well-defined crystallinity, as revealed by the electron diffraction patterns of selected areas (SAED) (Figure 12b). The chemical data of the individual single fibres were consistent with the chemical composition of chrysotile (Mg\({}_{3}\)Si\({}_{2}\)O\({}_{5}\)(OH)\({}_{4}\)) (Figure 12b). Other minor and trace elements are known to be present in the chrysotile samples [16], as also confirmed by the geochemical data of the origin rock (Table 2). However, due to the instrumental limits, these elements were below the detection limit. Serpentine lizardite was crystallised in lamellar sheet form (plate-like morphology) and, as seen by TEM, showed easy splitting in the sheets (Figure 12d).  
Figure 11: **(a)** Transmission electron micrographs of polypropylene filter residues indicated by blue arrow and talc indicated by red arrow; **(b)** spherical morphology of polypropylene filter residues.
# Superpixel Segmentation Using Weighted Coplanar Feature Clustering on RGBD Images
## 4 Weighted Coplanar Feature Clustering  
In this section, we will discuss the design and calculation process of the geometric distance (\(d_{g}\)) in detail as well as its coefficient, called the content-adaptive weight (\(\lambda_{b}\)). As mentioned above, the design of the distance measure is the core of linear iterative clustering frameworks. Likewise, geometric distance plays a key role in distance measure. Therefore, geometric distance should take full advantage of the information in depth maps while limiting the computational complexity. To that purpose, we designed a coplanar feature containing both color and shape elements, which only yields a scalar result, to measure the distance between pixels in 4D space. Moreover, a content-adaptive weighting system was designed and introduced to weight the coplanar feature. Furthermore, one component of the coplanar feature is PPL, which consists of a normal map. Therefore, for the sake of convenience, we elaborate this content in the following order: first, we introduce a fast generation method of a normal map. Second, we discuss the geometric interpretation of PPL and its formation using a normal map. Third, we discuss how to make up the coplanar feature. Fourth, we introduce the content-adaptive weight, which makes the segmentation more precise. Finally, we demonstrate the convergence rate of our method. The workflow of geometric distance computation is shown in Figure 2.  
Figure 2: Workflow of the geometric distance computation. Color, normal and depth maps are extracted from RGBD images. The normal and depth maps are used to generate the plane projection length (PPL), and the color map is used to evaluate the content-adaptive weight. Furthermore, the coplanar feature is concatenated by the lab and the PPL. Finally, the geometric distance is produced by multiplying the content-adaptive weight and the coplanar feature.
# Effect of Harmonics on Ferroresonance in Low Voltage Power Factor Correction System--A Case Study
## 3 System Description  
The case study presented in this paper is conducted on the low voltage and medium voltage side of the transformer station in an industrial plant. The transformer station, the operating conditions in which ferroresonance occurs, and the measurement setup are described below.
# Towards a Cyber-Physical Range for the Integrated Navigation System (INS) System (INS)
## 3 The Composition of the INS
### INS Components
#### 3.1.4 Central Alert Management Human Machine Interface (HMI)  
\"Alert\" means an abnormal situation which requires attention. Alerts are divided into three categories, namely \"alarm\", \"warning\", and \"caution\". An \"alarm\" requires immediate action by the bridge team. A \"warning\" indicates a changed condition that may cause danger if no action is taken. \"Caution\" gives information about an out-of-the-ordinary condition. The bridge teams are informed of \"Alarms\" and \"Warnings\" through visual announcement and audible signals. They are informed of \"Cautions\" only through a steady visual indication [7]. Alarm indicators are called \"Central Alert Management Human Machine Interface (HMI)\" in Resolution MSC.252(83), where the functions of alarm indicators are enhanced. For instance, the operator should be able to monitor alarm history in the INS. Another example is that at least 20 recent alerts can be displayed in the Central Alert Management HMI [7].
## 2 Materials and Methods
### Spatial Autocorrelation Analysis
#### 2.9.2 Grid Map Delineation and Class Pixels Extraction  
In order to obtain a tight spatial correlation between the heat emitted from buildings (recorded by the thermal bands of satellite images) and the impervious surfaces represented by NDBI, and because the area of study was characterized by a large interference of green cover with built-up surfaces spread beneath it, the area was split into a network of sites(grid), in which each site consisted of a cell with an area of 1 km\({}^{2}\), within which all data were extracted as single values representing the cell's spectral properties. The 1 km\({}^{2}\) sized grid was assumed to represent all possible surface features (urban, date palm trees, bare lands) that were available within the oasis' metropolis. This was confirmed by direct observation of the city-structure aspects.  
The applied grid method used in this study could potentially assist in determining the heat emitted from sole urban surface cover and areas covered with sole vegetation, in addition to the amount of heat emitted by holistic surface covers (urban and vegetation). Hence, it could be used to determine the spatial delineation of the heat islands and relate them to spatial phenomena that have a direct impact on heat island formation and distribution, through applying spatial autocorrelation mechanisms. In addition, by applying spatial gridding, it was possible to study the spatial correspondences between the heat islands and urban complexes without the need for geometric correction of satellite images, which may be complex due to variability in the spatial resolution between the thermal and optical regions in the satellite image's spectrum.
## 4 Practical Implementation
### Continuation of Medical Nutrition Therapy After Transfer or Discharge to Home  
MNT is not only important during ICU/IMC-U stay, it is an essential part of both the post-ICU/IMC-U recovery phase and after hospital discharge [43]. While advances in medicine and improvements in the quality of care have reduced ICU mortality in recent years, the number of post-critically ill patients in rehabilitation has consequently increased [112]. Low oral intake (e.g., only 700 kcal/day for one week after extubation) has been reported to be an amount insufficient to cover the energy needs of 97% of patients [42; 76]. In this context, most critically ill patients would need continuous medium-length or long-term MNT (ONS, EN, PN, and SPN) coupled with exercise for rehabilitation. MNT can also be continued at home after hospital discharge. However, it is also important to consider the physical and mental abilities of patients and their relatives, in addition to the patient's living conditions, with the aim of achieving a supportive environment [44]. Educating the patient and his/her close relatives should be proposed to achieve the highest level of independence possible [44]. Follow up involving a multidisciplinary nutrition team helps to increase the quality of care, facilitating the adaptation of the therapy and the support in case of possible complications [44].
## 3 Results and Discussion
### Resticide Adsorption Studies
#### 3.3.2 Isotherm Adsorption Studies
Comparison of Pesticide Adsorption Capacity with That of Other Adsorbents Derived from Biomass Sources  
Table 7 shows the adsorption capacities obtained in previous studies for the adsorption of the pesticides studied in this work, i.e., acetamiprid and imidalcoprid, with adsorbents synthesized from different biomass sources. \begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline  & & & & & & & & & & & \\ \hline \multirow{2}{*}{**Sample**} & \multirow{2}{*}{**Pesticide**} & \multicolumn{3}{c}{**q\({}_{\text{exp}}\)**} & \multicolumn{3}{c}{**q\({}_{\text{tcer}}\)**} & \multicolumn{3}{c}{**q\({}_{\text{sat}}\times\mathbf{10^{5}}\)**} & \multirow{2}{*}{**n**} & \multirow{2}{*}{**R\({}^{2}\)**} & \multirow{2}{*}{**q\({}_{\text{tcer}}\)**} & \multirow{2}{*}{**q\({}_{\text{m}}\times\mathbf{10^{-5}}\)**} & \multirow{2}{*}{**K\({}_{1}\times\mathbf{10^{6}}\)**} & \multirow{2}{*}{**K\({}_{2}\times\mathbf{10^{6}}\)**} & \multirow{2}{*}{**R\({}^{2}\)**} \\  & & **(mg/g)** & **(mg/g)** & **(m/g)** & **(L/mg)** & & & & & & & \\ \hline \multirow{2}{*}{**AC-ZnCl2**} & **ACT** & 128.9 & 115.3 & 125,557 & 3.96 & 0.9 & 0.958 & 112.9 & 8.87 & 3.06 & 3.06 & 0.955 \\  & **THM** & 126.8 & 124.9 & 1373 & 0.03 & 4.8 & 0.976 & 150.4 & 8.87 & 4.30 & 3.07 & 0.875 \\  & **IMD** & 166.1 & 149.3 & 50,683 & 0.29 & 1.5 & 0.972 & 164.6 & 8.87 & 4.23 & 3.07 & 0.972 \\ \hline \multirow{2}{*}{**AC-FeCl3**} & **ACT** & 113.9 & 83.7 & 2538 & 0.52 & 2.4 & 0.866 & 95.9 & 8.87 & 2.57 & 3.06 & 0.894 \\  & **THM** & 81.1 & 65.9 & 1927 & 0.31 & 2.6 & 0.949 & 75.7 & 8.87 & 1.83 & 3.06 & 0.898 \\  & **IMD** & 104.1 & 91.8 & 1360 & 20.30 & 1.7 & 0.968 & 103.6 & 8.87 & 2.58 & 3.06 & 0.960 \\ \hline \multirow{2}{*}{**AC-Fe(NO3)3**} & **ACT** & 60.5 & 37.2 & 243,432 & 9.57 & 0.6 & 0.791 & 34.2 & 8.87 & 0.81 & 3.06 & 0.768 \\  & **TIM** & 140.2 & 159.5 & 1378 & 0.12 & 4.8 & 0.929 & 160.5 & 8.87 & 4.88 & 3.07 & 0.679 \\  & **IMD** & 55.8 & 42.7 & 285,175 & 11.70 & 0.5 & 0.925 & 39.0 & 8.87 & 0.94 & 3.06 & 0.899 \\ \hline \multirow{2}{*}{**AC-Fe(SO4)3**} & **ACT** & 60.5 & 37.9 & 3740 & 169.1 & 0.5 & 0.793 & 34.2 & 8.87 & 8.10 & 3.06 & 0.768 \\  & **THM** & 140.2 & 139.1 & 139 & 108,617 & 0.4 & 0.950 & 160.5 & 8.87 & 4.88 & 3.07 & 0.678 \\ \cline{1-1}  & **IMD** & 55.8 & 42.7 & 7231 & 104.4 & 0.5 & 0.926 & 38.7 & 886,713 & 0.93 & 3.06 & 0.899 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Isotherm model parameters for the adsorption of ACT, THM, and IMD onto the carbon materials: the Sips and Guggenheim–Anderson–De Boer (GAB) models.
# Rapidly Deployable IoT Architecture with Data Security: Implementation and Experimental Evaluation
## 3 Lab-in-a-Box (LiB) Architecture
### Modular System Architecture
#### 3.1.6 Cloud Server  
Cloud server is responsible for facilitating the end-users' ability to access the sensed data. This is achieved by implementing several services including, but not limited to, data storage, data analytics, and data visualization in addition to providing an appropriate application program interface (API) and software tools through which the end-user can access the data. It also provides a user interface to display the data and interact with the network. Often cloud servers host an array of tools to sort and interpret the accumulated data. Security and privacy to data are one of the most common attributes to be considered in IoT applications, and accordingly, the most common platforms account for the same. Platforms such as Evrtything [55], Thingworx [56], and Paraimpu [57] require an API key in the HTTP header, and an open standard for authorization. One appealing aspect of the majority of the systems is that web mashups can be created in any programming language supporting HTTP communication. Overall, each application management system has its own unique advantages and could be chosen based on the specific IoT application.
# Provably Secure ECC-Based Anonymous Authentication and Key Agreement for IoT
###### Abstract  
With the rise of the Internet of Things (IoT), maintaining data confidentiality and protecting user privacy have become increasingly challenging. End devices in the IoT are often deployed in unattended environments and connected to open networks, making them vulnerable to physical tampering and other security attacks. Different authentication key agreement (AKA) schemes have been used in practice; several of them do not cover the necessary security features or are incompatible with resource-constrained end devices. Their security proofs have been performed under the Random-Oracle model. We present an AKA protocol for end devices and servers. The proposal leverages the ECC-based key exchange mechanism and one-way hash function-based message authentication method to achieve mutual authentication, user anonymity, and forward security. A formal security proof of the proposed scheme is performed under the standard model and the eCK model with the elliptic curve encryption computational assumptions, and formal verification is performed with ProVerif. According to the performance comparison, it is revealed that the proposed scheme offers user anonymity, perfect forward security, and mutual authentication, and resists typical attacks such as ephemeral secret leakage attacks, impersonation attacks, man-in-the-middle attacks, and key compromise impersonation attacks. Moreover, the proposed scheme has the lowest computational and communication overhead compared to existing schemes.  
authentication and key agreement; anonymity; Internet of Things; standard model; elliptic curve cryptography +
Footnote †: journal: Applied sciences
## 4 Experimental Results and Discussion
### End-to-End Delay  
The comparison of the end-to-end delays of the proposed CERP and existing approaches is represented in Figure 9 and Table 5. This method attained a lower delay in 100 nodes (0.05 s), and the time delay increased as the number of nodes increased in the WSN. For 100 nodes, the time delays of the two existing methods, the cross-layer-based adaptive threshold technique and cross-layer fuzzy logic, are 1.97 s and 2.94 s, respectively.  
Figure 8: Performance of end-to-end delay.  
Figure 9: Comparison of the packet delivery ratio.
# The Role of PET and SPECT Imaging in Prostate Cancer Targeted Alpha Therapy: When and How?
### Metastatic Prostate Cancer's Therapy  
Unfortunately, in 20-30% of cases, the tumor goes toward progression even with hematistosterone level below 20 ng/dL [2] and become castration-resistant PCa (CRPCa). Researchers demonstrated that the resistance to ADT occurs after approximately 10-15 months [8]. However, the overall survival in these patients has improved over time. In fact, the reactivation of AR signaling is seen in CRPCa. AR is targeted by androgen synthesis inhibitors (abiraterone) and AR-ligand inhibitors (enzalutamide, apalutamide, and daroglutamide). However, when a tumor becomes highly glycolytic and responsive to \({}^{18}\)F-fluorodeoxyglucose (\({}^{18}\)F-FDG), resistance to these agents and progression to aggressive disease can be observed. Another mechanism of resistance concerns the differentiation of AR-indifferent carcinoma such as neuroendocrine, which has a significant \({}^{18}\)F-FDG uptake and leads to the loss of AR signaling [9]. Moreover, some studies demonstrated how androgen ablation itself can reactivate AR signaling pathways. Finally, the AR heterogeneity expression that pre-exists in treatment-naive primary tumors could explain the resistance to AR signaling inhibitors (ARS) [10]. These are the basis that explains patients' relapse and therapeutic failure. The therapeutic resistance can be overcome using cytotoxic agents targeting the microtubules as a first-line chemotherapy. For metastatic castration-resistant PCa (mCRPC), Docetaxel, a taxane chemotherapy, was the first drug to be approved in 2004, which was followed by another taxane chemotherapy, Cabazitaxel. In 2010, the FDA approved in the United States Sipuleucel-T, a vaccine therapy targeting prostatic acid phosphatase (PAP), for asymptomatic and minimally symptomatic patients with mCRPC. The FDA authorized the anti-PD1 immune checkpoint inhibitor pembrolizumab in 2017 for the treatment of unresectable or metastatic solid cancers that have progressed after receiving conventional therapy and have microsatellite instability (MSI-H) or a defect in mismatch repair (dMMR) [11; 12]. In 2020, Olaparib and vacuolar, which target PARP, were approved by the FDA for mCRPC with genomic mutations on homologous recombination (HR) DNA repair genes which are present in up to 20% of mCRPC patients [4]. PTEN loss is present in 40-60% of mCRPC tumors and leads to hyperactivation of the PI3K-Akt-mTOR signaling pathway. Since there has been evidence of a cross-talk between the PI3K-Akt-mTOR pathway and AR signaling, the Phase 3 IPATential150 study with first-line mCRPC patients examined the combination of abiraterone with the Akt inhibitor ipatasertib. Patients taking abiraterone with ipatasertib saw a significant radiological PFS advantage, according to the research [13].
## 3 Methodology
### Methodology and Autoencoder Formulation  
As we mentioned before, we approach vessel segmentation as a classification problem. Indeed, in the context of our work, we built a convolutional neural network, and more specifically, an autoencoder, which classifies the pixels of a given fundus image to be either vessel or non-vessel pixels. Our model was trained using supervised learning, meaning that the manually segmented images helped our network to learn how to detect the vessels more easily (see Figure 4 for an overview of the process). In the following two sections, we explain the theoretical background of this unique type of Neural network and present the layers of our proposed structure.
## 3 LSTM Network Optimized by GA  
### LSTM Network Model  
The LSTM neural network solves the problem of gradient disappearance and the explosion of traditional recursive neural networks linked by network units in a chain way, which can effectively improve the learning time. In dealing with the prediction of time series and nonlinear mapping problems, the LSTM model with memory ability shows strong advantages [22]. A structure called a memory cell is added to LSTM to memorize past information, and three gate structures, input gate, output gate, and forget gate, are added to control the transmission of historical information [31].  
The structure of the LSTM neural network is shown in Figure 1. Supposing that the network input is \((x_{1},x_{2},\ldots,x_{T})\) and the hidden-layer state is \((\mathbf{h}_{1},\mathbf{h}_{2},\ldots,\mathbf{h}_{T})\), at time t, the calculations of each unit and gate are shown in Equations (3)-(8):  
\[\mathbf{i}_{t} =\sigma(\mathbf{w}_{1}\cdot[\mathbf{h}_{t-1},\mathbf{x}_{t}]+b_{1}) \tag{3}\] \[\mathbf{f}_{t} =\sigma(\mathbf{w}_{t}\cdot[\mathbf{h}_{t-1},\mathbf{x}_{t}]+b_{t})\] (4) \[\widetilde{\mathbf{c}}_{t} =\tanh(\mathbf{w}_{c}\cdot[\mathbf{h}_{t-1},\mathbf{x}_{t}]+b_{c})\] (5) \[\mathbf{c}_{t} =f_{t}\circ\mathbf{c}_{t-1}+\mathbf{i}_{t}\circ\widetilde{\mathbf{c}}_{t}\] (6) \[\mathbf{o}_{t} =\sigma(\mathbf{w}_{0}\cdot[\mathbf{h}_{t-1},\mathbf{x}_{t}]+b_{0})\] (7) \[\mathbf{h}_{t} =\mathbf{o}_{t}\circ\tanh(\mathbf{c}_{t}) \tag{8}\]  
In Formulas (3)-(8), \(\mathbf{i}_{t},\mathbf{f}_{t}\), and \(\mathbf{o}_{t}\) are the calculations of input gate, forget gate, and output gate, respectively; among them, the input gate is mainly used to determine how much input information at the current moment is retained to the unit state at the current moment; the forget gate is mainly used to determine how much information of the unit state \(\mathbf{c}_{t-1}\) from the previous moment is retained in the current cell state \(\mathbf{c}_{t}\); the output gate is mainly used to determine how much output the current cell state has. \(\mathbf{h}_{t}\) is the final output of the network, \(\widetilde{\mathbf{c}}_{t}\) is the current input unit state; \(\mathbf{c}_{t}\) is the current moment unit state; \(\mathbf{w}_{i}\), \(\mathbf{w}_{t}\), \(\mathbf{w}_{c}\), and \(\mathbf{w}_{0}\) are the weight matrices of the three gates and unit states; \(b_{i}\), \(b_{t}\), \(b_{c}\), and \(b_{0}\) are respectively the bias of each gate and unit state; \(\sigma(\cdot)\) and \(\tanh(\cdot)\) are transfer functions; \(\cdot\) represents the vector inner product; and the symbol \(\circ\) represents element-wise multiplication.  
Figure 1: The LSTM neural network structure.
# Innovative Integration of Severe Weather Forecasts into an Extended Arrival Manager
## 3 AMAN Air Traffic Controller Support
### Arrival Sequencing  
For sequence planning, the shortest possible approach routes with the highest possible approach speeds are determined, as well as the longest routes with the lowest speeds. These  
Figure 2: The principle of diversion route calculation. \(P_{0}\), \(P_{1}\), and \(P_{2}\) are points outside a weather polygon and represent the possible start and end points of the diversion. All waypoints on the waypoint list between them will be dismissed. Additionally, selected diversion waypoints such as \(IP\)\([P_{1}P_{2}]^{\text{right}}\) will be integrated and used for 4D trajectory generation.  
result in an earliest and a latest landing time for each aircraft, and a target time window can thus be planned. The sequence is now determined by using the shortest possible approach times for each aircraft. If two aircraft fall below their weight class-dependent minimum separation, the succeeding aircraft is moved back in time until the minimum separation is reached on final and for touchdown. If an aircraft is shifted behind its latest possible landing time, it must be guided into a holding. This finally results in the landing times for the precise trajectory calculation.