esa-sceva/satcom-chunk-collection huggingface dataset:
# Second-Order Statistical Properties of Vehicle-to-Vehicle Rician Fading Channel
## 3 Derivation of (18) and (21)  
Utilizing the same process described above, the expression for \(b_{1}^{DB}\) was derived with \(m=1\), and hence closed-form expressions for the terms (\(Y_{9}\) and \(Y_{10}\)) can be obtained.  
\[\begin{split} Y_{9}=\frac{f_{Tmax}}{2\pi b_{1}(k_{T})}\int\limits_ {\pi}^{\pi}\left[e^{k_{T}cos\beta_{T}}\times\cos\left(\beta_{T}+\mu_{T}-\gamma _{T}\right)d\beta_{T}=f_{Tmax}\cos\left(\mu_{T}-\gamma_{T}\right)\frac{I_{1}(k _{T})}{I_{0}(k_{T})}\right.\\ Y_{10}=\frac{f_{Tmax}}{2\pi b_{1}(k_{R})}\int\limits_{\pi}^{\pi} \left[e^{k_{R}cos\beta_{R}}\times\cos\left(\beta_{R}+\mu_{R}-\gamma_{R}\right)d \beta_{R}=f_{Rmax}\cos\left(\mu_{R}-\gamma_{R}\right)\frac{I_{1}(k_{R})}{I_{0 }(k_{R})}\right.\\ b_{1}^{DB}=(2\pi)b_{0}^{DB}[f_{Tmax}\cos\left(\mu_{T}-\gamma_{T} \right)\frac{I_{1}(k_{T})}{I_{0}(k_{T})}+f_{Rmax}\cos\left(\mu_{R}-\gamma_{R} \right)\frac{I_{1}(k_{R})}{I_{0}(k_{R})}]\\ b_{1}^{DB}=(2\pi)b_{0}^{DB}[Y_{9}+Y_{10}]\end{split} \tag{19}\]  
Applying the same process outlined earlier, the expression for \(b_{2}^{DB}\) can be obtained with \(m=2\). Thus, the close-form expressions for the terms (\(Y_{11}\), \(Y_{12}\), and \(Y_{13}\)) are given below:  
\[\begin{split} Y_{11}=\frac{f_{Tmax}^{2}}{2\pi b_{1}(k_{T})}\int \limits_{-\pi}^{\pi}\left[e^{k_{T}cos\beta_{T}}\times\cos^{2}(\beta_{T}+\mu_{ T}-\gamma_{T})d\beta_{T}=f_{Tmax}^{2}[\frac{I_{0}(k_{T})+\cos 2(\mu_{T}-\gamma_{T})I_{0}(k_{T})} {2I_{0}(k_{T})}]\right.\\ Y_{12}=\frac{f_{Tmax}^{2}}{2\pi b_{1}(k_{R})}\int\limits_{-\pi}^{ \pi}\left[e^{k_{R}cos\beta_{R}}\times\cos^{2}(\beta_{R}+\mu_{R}-\gamma_{R})d \beta_{R}=f_{Rmax}^{2}[\frac{I_{0}(k_{R})+\cos 2(\mu_{R}-\gamma_{R})I_{0}(k_{R})}{2 \pi b_{0}(k_{R})}]\right.\\ Y_{13}=2f_{Tmax}f_{Rmax}[\frac{1}{2\pi b_{1}(k_{T})}\int\limits_ {-\pi}^{\pi}\left[e^{k_{T}cos\beta_{T}}\times\cos\left(\beta_{T}+\mu_{T}- \gamma_{T}\right)d\beta_{T}\times\frac{1}{2\pi b_{1}(k_{R})}\int\limits_{-\pi }^{\pi}[e^{k_{R}cos\beta_{R}}\times\cos(\beta_{R}+\mu_{R}-\gamma_{R})]d\beta_{ R}\right.\\ =2f_{Tmax}f_{Rmax}\cos(\mu_{T}-\gamma_{T})\frac{I_{1}(k_{T})}{I_{ 0}(k_{T})}\cos(\mu_{R}-\gamma_{R})\frac{I_{1}(k_{R})}{I_{0}(k_{R})}\\ b_{2}^{DB}=(2\pi)^{2}b_{0}^{DB}[Y_{11}+Y_{12}+Y_{13}].\end{split} \tag{20}\]
## 5 Discussion  
The range of the estimation error is summarized in Table 3.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Parameter**} & \multirow{2}{*}{**Equation**} & \multicolumn{2}{c}{**Below the Range**} & \multicolumn{2}{c}{**Within the Range**} & \multicolumn{2}{c}{**Above the Range**} \\ \cline{3-8}  & & \(L\)/\(D_{\text{S}}\) \textgreater{} 7.5 & \(L\)/\(D_{\text{S}}\) \textless{} 7.5 & \(L\)/\(D_{\text{S}}\) \textgreater{} 7.5 & \(L\)/\(D_{\text{S}}\) \textless{} 7.5 & \(L\)/\(D_{\text{S}}\) \textgreater{} 7.5 & \(L\)/\(D_{\text{S}}\) \textless{} 7.5 \\ \hline \multirow{8}{*}{\(C_{Rf}\)} & Equation (17) & \multirow{2}{*}{10–15} & \multirow{2}{*}{11–13} & \multirow{2}{*}{7–9} & \multirow{2}{*}{6–7} & \multirow{2}{*}{6–10} & \multirow{2}{*}{1–11} \\  & (\(N\!LLS\)) & & & & & & \\ \cline{1-1} \cline{3-8}  & Equation (18) & & & & & & \\ \cline{1-1} \cline{3-8}  & (\(ANN\)) & & & & & & \\ \cline{1-1} \cline{3-8}  & Equation (19) & & & & & & \\ \cline{1-1} \cline{3-8}  & (\(ANN\)) & & & & & & \\ \cline{1-1} \cline{3-8}  & (\(ANN\)) & & & & & & \\ \cline{1-1} \cline{3-8}  & (\(ANN\)) & & & & & & \\ \cline{1-1} \cline{3-8}  & (\(ANN\)) & & & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 3: The range of the estimation error (%) for the velocities below, within, and above those used for the training.  
Figure 15: Comparison of the estimation error of the resistance coefficient of the different cases of the submarines for some transverse speeds, (**a**) \(V_{t}\) = 0.02 m/s, (**b**) \(V_{t}\) = 0.1255 m/s, (**c**) \(V_{t}\) = 0.375 m/s, (**d**) \(V_{t}\) = 0.6 m/s.  
The following conclusions can be made on the accuracy of the procedures for estimating the resistance coefficient:  
For the velocities lower than the range used for training, \(ANN\) with \(NON=6\) shows much better ability to estimate the resistance coefficient for the forward direction compared to the equations. Surprisingly, for transverse movement the \(NLSS\) performs better than the \(ANN\).  
For forward velocities higher than those used for the training, \(ANN\) with optimal \(NON\) has the lowest error. However, for transverse directions, differences between equations and \(ANN\) are small.  
For transverse velocities within training range, \(ANN\) with \(NON=7\) is more accurate than the equations. For forward motion, however error of Equation (19) is close to the error of \(ANN\) with \(NON=6\).
# Whole-Genome Alignment: Methods, Challenges, and Future Directions
## 5 Comprehensive Analysis of Genomic Comparison Tools: Human and Diverse Genomes
### Execution Times and Tool Complexity  
In our comparative analysis of genomic alignment tools applied to human genomes, we observed significant variations in execution times, reflecting each tool's computational approach and efficiency. MUMmer4 took approximately 15 h and 34 min, indicating its intensive computational process, especially effective for aligning highly similar sequences. Sibeliaz completed its analysis of the human genomes in about 6 h and 19 min, demonstrating its efficiency in handling complex k-mer pattern analysis despite the large data size. For the comparison between human and mouse genomes, the analysis took 22 h, indicating a longer processing time due to the increased complexity of comparative genomic analysis. Dialign2 faced challenges with the human genome size, leading to a crash and underscoring the need for more scalable solutions in genomic analysis tools. Minimap2 stood out for its speed, completing the mapping in just 44 min, demonstrating its capability for rapid and efficient genomic alignment. These varying execution times are indicative of the underlying algorithms and computational strategies employed by each tool, highlighting the importance of considering both accuracy and efficiency when selecting genomic analysis tools for large-scale studies (Figure 13). GraphAlign consistently failed during the analyses, probably due to computational constraints, especially when aligning large genomes used for analyses (human vs. human and human vs. mouse).
# Evaluating and Managing the Microbial Contamination of Eye Drops: A Two-Phase Hospital-Based Study
## 1 Introduction
The Pharmacy Department at the Assistance Publique des Hopitaux de Paris (APHP), France, has a dedicated unit for preparing different preservative-free eye drops for hospital and patient use. Specifically, the Sterile Ophthalmological and Oncological Preparations Unit (UPSO2) is a center of excellence that thrives on maintaining sterility throughout the supply chain to ensure patient and medication safety. The manufacturing process follows the recommendations of Good Preparation Practices (GPP) [37] and the _European Pharmacopoeia_, 11 th edition, chapter 5.1.1. [34]. It is imperative that eye drops remain sterile throughout the entire manufacturing process and supply chain. Moreover, this sterility should be preserved for the recommended duration of use, from opening to handling by the end user [8; 9]. Within this context, UPSO2 evaluated an innovative green technology proposed by Pylote SAS. This technology is incorporated into eye drop tip and cap materials. Notably, the first eye drop container was marketed, as of 2022, collaboratively with the packaging supplier Berry Global. This first-to-market multidose antimicrobial dropper, called Activated RispharmTM, is designed to help prevent microbial eye infections in patients. Importantly, ophthalmic droppers activated by Pylote technology allow a hygienic application, reduce plastic waste, and are fully biocompatible while being certified as non-irrittant and non-cytotoxic technology [38]. To evaluate the effectiveness, applicability, and practical use of eye drops activated by Pylote technology, the Pharmacy unit--UPSO2--conducted a two-phase study, as outlined in this article. The first phase assessed the risk of contamination of preservative-free in-usestandard eye drops when used by patients and practitioners and identified the possible contaminating microorganisms, taking into account the microbial contamination of eye drops at the cap, tip, and content levels. Subsequently, the second phase was carried out in vitro, comparing the evolution of the artificial microbial contamination of caps and tips between eye drops in standard packaging and those in activated packaging (activated caps and tips, standard bottle) with Pylote technology. Additionally, the study evaluated the practical application and use of the activated packaging. Ultimately, the aim is to validate the effectiveness of green technology in preventing microbial contamination, allowing practical use, and ensuring patient safety.
## 3 Materials and Methods
### Wireless Temperature Sensors  
To track temperature changes, we used custom-built wireless temperature sensors installed in selected places inside the building. Each sensor has the following subsystems [35]: sensing (temperature measurement), computing (data processing), communication (long-range radio communication), and power (providing system supply voltage). Temperature is measured by a Sensirion temperature sensor [36] which digitalizes readouts and transmits them using an Inter-Integrated Circuit (I\({}^{2}\)C) interface to a microcontroller. The selected microcontroller responsible for data processing belongs to the STM32L1 family of ultra-low-power System-On-Chips (SOC), which has the following features [37]:  
* Ultra-low-power 32-bit Arm-based Cortex-M0+;
* 10 KB RAM, 32 KB Flash, 4 KB of data EEPROM;
* \(-40\) \({}^{\circ}\)C to 85 \({}^{\circ}\)C operating temperature range;
* Supply current 1.2 \(\mu\)A in stop mode with a real-time clock;
* 1.65 V to 3.6 V supply voltage;
* USB peripheral communication interface.  
Communication is provided by a long-range (LoRa) radio module from Semtech [38] that operates in the 433 MHz band. The device is powered by a lithium battery. The detailed documentation of the devices, including schematics, PCBs, and source codes, is described in [39]. Several versions of the devices have been constructed and tested before installation in the building--one of them is shown in Figure 4.  
Figure 4: A battery-powered wireless temperature sensor and an edge router with Ethernet interface.  
The effort related to design and implementation of dedicated temperature sensors was justified by the ease of configuration of measurement intervals, full control over encryption mechanisms securing data transmission, and flexibility in adjusting radio transmission power.
## 2 Related Work
### Clustering Schemes in CCN  
For the dissemination of content, a deep-learning-based approach is used in [13]. It consists of three phases. In the first phase, the probability of a stable vehicle connection is determined to identify vehicle pairs using the Wiener process. In the second phase, a convolutional neural network is used to find the social relationship, e.g., between pairs of vehicles. The social relationship between vehicles is estimated for optimal network performance; for example, data held by vehicle V_j requested by Vehicle V_i should not be broadcasted. A convolutional neural network learns automatically from the values of its filter to identify vehicle community/social relationships. In the last phase, once a V2V pair is confirmed, the content is disseminated. Based on social similarity in [14], cooperative caching is studied considering vehicle mobility. Furthermore, the content replacement policy is investigated considering the popularity of the content, i.e., the time between requesting the same content.  
The scheme proposed in [15] uses the Markov model to predict the mobility direction of vehicles. The clusters are made based on distance, the number of hops, link expiration time, and the probability that neighbors maintain their status. In the case of head vehicle changing its location, the head vehicle selection process starts again. The author in [16] proposed a cluster-based device mobility management scheme. The complete cluster information is maintained in the cluster head vehicle. Before leaving the cluster, the provider vehicle forwards the current location information to the cluster head to satisfy the pending interests.  
The research paper [17] discusses the various approaches of cluster vehicular networks. In [18], two techniques, fuzzy C mean and Q-LEACH, are combined to cluster the vehicles in the network. The optimal cluster head vehicle is selected based on the distance and degree of the neighbor vehicle. This information is collected through sensor nodes. It achieves a lower latency rate and higher throughput. In the research paper [19], the vehicles with the same trajectory made a cluster to ensure successful communication. The cluster head (CH) was selected based on direction and speed information. In the case of a request, the RSU forwards required data to CH to cache the data and forward them on request to the provider. The authentication of the cluster and data reliability is not provided.
## 2 Materials and Methods
### Local and Global Capabilities of the IoT Devices  
**Definition of Capability**  
Definition: Within The Open Group, a capability is defined as \"the ability to perform a certain action or task\" [7]. A business capability refers to a specific ability or capacity that a business possesses or utilizes to achieve a particular purpose or desired outcome.  
Building upon the aforementioned definition, we can extend its application to a broader range of solutions. For the purpose of this article, the term \"capability\" is used to describe the ability of an IoT device to perform certain actions or carry out required operations.  
The term \"capability\" can be applied not only to individual IoT devices but also to the network of IoT devices. While software components primarily rely on algorithms to define their capabilities, IoT devices integrate specific algorithms with physical components that are collectively designed to fulfill particular functions.  
In both cases, the capabilities are closely intertwined with the functional requirements of the IT system.  
**Local Capability**  
Local or elementary capability pertains to a single unit of the given operation. For example, in the case of a thermometer, its ability to measure temperature constitutes a local capability specific to the thermometer. Local capabilities are inherently tied to a specific location, whether it be software components or a physical device.  
**Global Capability**  
In contrast to local capabilities, which are inherently tied to a specific location, global capabilities, also known as complex capabilities, are associated with the network of IoT devices. Expanding on the example of temperature measurement, the global capability of an IoT network to measure temperature is not linked to a particular thermometer but rather encompasses all temperature sensors within that network.  
In the subsequent sections, the capabilities of IoT devices and IoT networks will be represented in the form of graphs, and any modifications or enhancements to the devices or networks will be depicted as graph transformations.
# In-Vitro Experimental Modeling of Oscillatory Respiratory Flow in a CT-Scanned OSAHS Tract
## 1 Introduction
The recent numerical observations [18; 19] of the unsteady flow patterns differ significantly from the steady ones in the ETA models. In-vitro measurement of unsteady respiratory flow in human airway models poses a great challenge for fluid experimentalists due to related geometrical complexity, limited optical accessibility and oscillatory flow rate [20]. Specifically, three difficulties need to be overcome if an accurate and reliable mimicking of respiratory flow in in-vitro environment was required, i.e., a realistic respiratory tract model with OSAHS, good optical index matching, and reliable control over the oscillatory respiratory flow rate. It is worth noting that most investigations on oscillatory respiratory flow were based on the simplified sin curve flow rate as the oscillatory boundary condition [18; 19]. In fact, the realistic oscillatory respiratory boundary condition is quite different from the sin function [21]. The goal of the present research is twofold. The primary goal is to tackle the above mentioned three critical difficulties arising in the in-vitro experimental modeling of OSAHS flows. The secondary goal is to employ the experimental rig to measure the oscillatory respiratory flows in an ETA model with OSAHS. The ETA model with OSAHS is composed of an oral cavity, pharynx, larynx and trachea with total closure of the nasal passageway. The remainder of this paper is organized as follows. In Section 2, an in-vitro experimental model will be described, focusing on techniques to overcome the above three difficulties. Section 3 will present PIV techniques tailored to the experimental rig. Section 4 will present the PIV measurement results of oscillatory respiratory flow in the ETA model with OSAHS and their clinical implications. Concluding remarks will be made in Section 5.
## 2 The Evolution from Cloud Computing to MEC
### Edge Computing
#### 2.3.1 Definition of Mobile Edge Computing  
As maintained by European Telecommunications Standards Institute (ETSI) [48], mobile edge computing \"provides an IT service environment and cloud computing capabilities at the edge of the mobile network, within the Radio Access Network (RAN) and in close proximity to mobile subscribers\". MEC changes how mobile data are handled by connecting users directly to a nearby cloud platform, avoiding direct travel through the core network. The RAN helps with this connection in the cellular communication system. MEC is like an upgrade for mobile Base Stations (BSs), blending information technology and telecommunications [48]. It is seen as a crucial part of the future 5G networks, working alongside technologies such as Network Function Virtualization (NFV) and Software-Defined Networking (SDN) to transform mobile broadband into a programmable space that meets the specific needs of 5G, such as speed, delay, scalability, and automation [49].
# Multifrequency Impedance Method Based on Neural Network for Root Canal Length Measurement
## 2 Material and Methods
### Feature Selection  
The data were augmented using combinations of multiple frequencies. The accuracy of root canal length measurement could be increased after the sufficient impedance ratios were available. Features were selected according to the divergence and correlation between features and goals [20]. Filtering is one of the most commonly used methods for feature selection in deep learning in order to develop a rule to measure each feature and sort all features by their importance with respect to the target attribute. The first step was  
Figure 4: Diagram of the data augmentation.  
Figure 5: An example of data augmentation.  
to calculate the variance of each feature, where features with variance below the threshold were deleted. The next step was to calculate the correlation coefficient of each remaining feature to the label. In addition, the features for the training model also required the numerical characteristics of the measuring conditions, such as tooth and file types. We used one-hot encoding to convert these non-numerical attributes into numerical features, as shown in Table 1.  
Among the large amount of augmented data (i.e., impedance ratios), based on the model performance and convenience of calculation, the top ten groups of impedance ratios--as ranked by the correlation coefficient--were selected (5 kHz/0.5 kHz, 8 kHz/0.5 kHz, 10 kHz/0.5 kHz, 12 kHz/0.5 kHz, 15 kHz/0.5 kHz, 20 kHz/0.5 kHz, 8 kHz/1 kHz, 10 kHz/1 kHz, 15 kHz/1 kHz, and 20 kHz/1 kHz), together with tooth type and file type, as the features for the model.
## 2 Materials and Methods  
### Patient Selection and Data Collection  
Clinical records of biopsy-proven DLBCL patients diagnosed between September 2008 and October 2019, referred to the Oncobematology Division of our Institute immediately after diagnosis for standard-of-care therapy, were retrospectively evaluated. Inclusion criteria were: (1) baseline \({}^{18}\)F[FDG] PET/CT performed at our institute prior to any treatment for DLBCL; (2) patients suitable for standard-of-care first-line chemotherapy; (3) availability of all clinical, pathology, and imaging data; (4) PET images acquired with 3D acquisition modality and 256 \(\times\) 256 matrix; (5) no lost follow-up. Patients were excluded in case of parameters impairing quantitative image analysis (e.g., wrong input of injection data, glycemia >200 mg/dL, para-vein injection). The study was approved by the institutional ethical committee and scientific review board (ID trial 2863).  
Among 376 patients identified from clinical records, one hundred and twelve patients (\(n=112\)) were considered for the analysis according to inclusion/exclusion criteria. Among them, 9 patients were classified as tumor stage I, 31 patients as stage II, 8 patients as stage III, and 64 patients as stage IV. For 9 patients the prognostic score was IPI = 0, for 33 patients IPI = 1, for 28 patients IPI = 2, for 29 patients IPI = 3, for 12 patients IPI = 4, and for 1 patient IPI = 5. All patients were treated with first-line R-CHOP/R-CHOP-like chemoimmunotherapy, according to the best standard-of-care clinical practice at ourinstitution and to international guidelines. Population characteristics and survival data are reported in the Supplementary Materials together with PET acquisition parameters (Tables S1-S3).  
\({}^{18}\)F-FDG PET/CT acquisitions were performed on a Discovery ST or a Discovery 600 PET-CT scanner (GE Healthcare, Waukesha, WI, USA), according to international procedural guidelines [17]. PET images acquired on the Discovery ST scanner were reconstructed with OSEM algorithm VUE point (2 iterations, 30 subsets, and 4.5 mm Gaussian post-filter), whereas those acquired on the Discovery 600 scanner were reconstructed with OSEM algorithm VUE point HD (2 iterations, 16 subsets, and 5 mm Gaussian post-filter).  
Progression-free survival (PFS) was calculated from the date of the diagnosis to the date of progression or last follow-up, whichever occurred first. Overall survival (OS) was calculated from the date of the diagnosis to the date of death or last follow-up, whichever occurred first. Specifically, no deaths occurred as the first event, thus, they were not counted in the definition of PFS. Follow-up was updated in March 2021.