esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Results and Discussion  
### Molecular Dipolar Polarizability Model  
The derivative of a molecular dipolar-polarizability tensor \(\alpha\)', with respect to the normal coordinates of vibration \(Q\), is obtained as:  
\[\alpha^{\prime}=-\sum_{i,j,k,l}\mathbf{B}^{y}\cdot\left(\mathbf{A}^{jk}\right) \cdot\mathbf{B}^{\mu} \tag{7}\]  
where (\(\mathbf{A}^{jk}\))' is the derivative of \(\mathbf{A}^{jk}\) with respect to \(Q\), \(\mathbf{A}^{jk}\) the inverse polarizability matrix \(\mathbf{A}\mathbf{\mu}=\mathbf{E}\), \(\mathbf{\mu}\) the induced dipole moment vector, \(\mathbf{E}\) the applied electric field vector, and \(\mathbf{B}=\mathbf{A}^{-1}\) the _many-body_ polarizability matrix. The (\(\mathbf{A}^{jk}\))' can be calculated for \(j\
eq k\), from the matrix of transformation from a  
Figure 1: Polarizability of CH\({}_{4}\) as a function of polarizabilities of H and C [4]. Units are Å\({}^{3}\). Drawn with subroutine THREED ([PERSON], Iowa State University Computation Center). The vertical coordinate was truncated at 7 and –2.  
Cartesian coordinate system in normal coordinates, for the considered vibration mode (\(\mathbf{A}^{ik}\) = \(\mathbf{T}^{ik}\); \(\forall j\
eq k\), where \(\mathbf{T}^{ik}\) is the gradient tensor of the electric field \(\mathbf{E}\).  
However, the diagonal elements (\(\mathbf{A}^{ii}\))' will be calculated, since the model must take into account the dependence of the atomic polarizabilities _vs._ the length of the bonds between atoms, which leads us to impose an indispensable _supplementary hypothesis_: the use of the vibrational correction of atomic polarizabilities to average molecular vibrational stretching modes. The modified atomic polarizabilities are calculated as:  
\[\alpha_{j}=\alpha_{j}^{\circ}+\sum_{n}\Biggl{(}\frac{d\alpha_{j}}{dR_{n}} \Biggr{)}R_{n} \tag{8}\]  
where \(R_{n}\) is the displacement of the \(n\)-th bond length from its experimental value in equilibrium, and \(\alpha_{j}^{\circ}\) the atomic polarizability of the \(j\) unit at the rest configuration of the molecule.  
By optimizing the \(d\alpha/dR_{n}\) relations to represent the best the experimental values of the derivative of the molecular polarizability tensor \(\alpha\)', to model the molecular polarizability variations during bond vibrations is possible.
## 2 Overview of IoT-Fog-Cloud Architecture  
We describe our IoT-Fog-Cloud architecture in detail in this section. IoT refers to devices that generate data, communicate with other devices in a real-world scenario, and have storage for configuration. Since DHT11 sensors are not equipped with network capabilities and storage for configuration, one DHT11 sensor will be linked to only one Raspberry Pi board in the IoT-Fog-Cloud architecture using GPIO pins. The DHT11 sensor connected to the Raspberry Pi will be considered an IoT device. We used the Raspberry Pi to provide storage for configuration and enable the Wi-Fi connectivity of the DHT11 sensor. Each IoT device will be linked to other Raspberry Pi devices through Wi-Fi, which served as a fog device in the fog layer.  
The MQTT protocol was utilized to communicate between IoT devices, fog nodes, and the cloud because the MQTT protocol is extensively used and supported by all IoT platforms and commercial sensors. The MQTT broker named Eclipse Mosquitto [13] was installed in the Raspberry Pi which served as a fog device. The Mosquitto MQTT broker is a server that receives all messages from IoT devices and publishes them to other devices using the MQTT protocol. Some advantages of using an MQTT broker include (1) facilitating scalability with a large number of IoT devices, (2) handling authentication credentials and certificates, (3) decreasing cellular network strain without compromising security, and (4) preventing unsafe and vulnerable devices from being connected. The Mosquitto MQTT broker used the subscribe-publish strategy described in [5] to exchange messages and filtered them based on the topics. The topics are UTF-8 strings used by the broker to identify the sensor data type (temperature degree or humidity degree). In our experiments, each data type (temperature degree or humidity degree) was considered a separate topic to provide consistent and accurate results. The three DHT11 sensor data gathered by the three IoT devices were sent through Wi-Fi to the Raspberry Pi that served as the fog device. The Raspberry Pi that acts as the fog device with the MQTT broker Mosquitto was connected through the Internet to the AWS cloud. The Internet served as the communication between the three layers.  
We conducted several experiments based on the three analysis methods in our previous work [6], and we found that the performance of the IoT-Fog-Cloud architecture outperforms the performance of the IoT-Cloud architecture. However, we found that the IoT-Fog-Cloud architecture does not meet the security requirements. Therefore, to fill the gap of security requirements between the IoT layer and fog layer and meet the authentication and authorization aims, first, we propose a blockchain-based certificate model of the IoT-Fog-Cloud architecture to fill the gap of the authentication problem. Second, we propose a fine-grained data access control model based on the ABE of the IoT-Fog-Cloud architecture to fill the gap of the authorization problem. Figure 1 shows an overview of the IoT-Fog-Cloud architecture and where the two security models are applied.
###### Abstract
The first data association step is employed to associate the measurements with the existing target. To do this, the AMTB filter first uses the gate technique to select the measurements falling inside the acceptance gates of individual existing targets from the measurements at time step \(k\) and then employs the two-dimensional assignment technique to assign the selected observations to individual existing objects. If a measurement is assigned to an existing object, the updated PDF of this existing object that is correlated to this observation is used as its PDF. If no measurement is assigned to an existing object, its predicted PDF is used as its PDF. The second data association step is employed to associate the measurements with the potential birth target. To do this, the AMTB filter selects the measurements falling inside the acceptance gates of individual potential birth targets from the unused measurements at time step \(k\) and then employs the two-dimensional assignment technique to assign the selected observations to individual potential birth objects. If a measurement is assigned to a potential birth target, this potential birth target becomes a newborn target. Moreover, this filter uses the unused observations at time steps \(k-1\) and \(k\) to form the potential birth targets in terms of the velocity, and uses the least squares technique to acquire the initial state of each potential birth target and its error covariance. Due to the use of the gating technique, the proposed filter obviates the need for prior clutter density, survival probability, initial state of the birth object and its error covariance. The simulation results demonstrate that the AMTB filter outperforms the AGLMB filter [22], adaptive CBMMeMber (ACBMeMber) filter [20], adaptive multi-Bernoulli (AMB) filter [19] and adaptive PHD (APHD) filter [17]. Our contribution in this article is that we propose an adaptive marginal multi-target Bayes filter without the need for clutter density. The main advantage of the proposed filter over the available adaptive filters is that it obviates the need for clutter density and survival probability that are required for the available adaptive filters. Identical to adaptive RFS-based filters, the proposed filter is applied to radar MTT systems. The structure of the article is as follows: the AMTB filter without the need for clutter density for a linear Gaussian noisy system is given in Section 2. An extension of this filter to nonlinear observations is provided in Section 3. The performance evaluation of the AMTB filter is given in Section 4 by comparing it with adaptive RFS-based filters. In Section 5, we provide the conclusions.
## 3 Proposed Methodology
### Data Acquisition
#### 3.1.1 CSI-HAR Dataset  
The proposed model's performance and comparable baseline models for WiFi-based HAR were evaluated using the publicly available CSI-HAR dataset [27]. The dataset was collected by building in the Nexmon tool on a Raspberry Pi-4 GB, which allowed for collecting and storing CSI data based on transmitted and received information. The dataset contains 4000 CSI samples collected over 20 s, with each line representing 5 ms. The activity-related parts of the data were separated and stored in CSV files as matrices with 52 columns and 600 to 1100 rows, depending on the activity time. Along with the CSI samples, label files were provided to distinguish the lines for each action. The dataset consists of seven discrete activities, namely walk, run, sit down, lie down, stand up, bend, and fall. These actions were operated a total of twenty repetitions by three participants across different age groups within a controlled homeroom environment.
###### Abstract  
As the carriers of massive data, data centers are constantly needed to process and calculate all kinds of information from various fields and have become an important infrastructure for the convenience of human life. Data centers are booming around the world, accompanied by the problems of high power consumption and poor heat dissipation. One of the most effective solutions to these problems is to adapt a two-phase liquid immersion cooling technology, which is a more energy-saving and efficient method than the traditional cooling methods; the reason for this is mainly that in two-phase liquid immersion cooling technology, the heat transfer caused by the phase change of liquid coolants (electronic fluoride liquids) helps to cool and improve the temperature uniformity of electronic components. However, the requirements for the electronic fluoride liquids used in two-phase liquid immersion cooling systems are strict. The thermophysical properties (saturated vapor pressure, density, surface tension, viscosity, thermal conductivity and latent heat of vaporization, etc.) of the liquid coolants play a very key role in the heat dissipation capacity of two-phase liquid immersion cooling systems. However, it is not always easy to obtain new electronic fluoride liquids under many actual conditions and reasonable prediction models of their thermophysical properties could contribute to the preliminary screening of the coolants. Thus, the prediction models of their key thermophysical properties (saturated vapor pressure, saturation density, surface tension, viscosity and thermal conductivity) are reviewed, and the accuracy and practicality of these prediction models in predicting the thermophysical properties of electronic fluoride liquids (FC-72, HFE-7100 and Novec 649) are evaluated. This work will provide a valuable reference for actual engineering applications.  
two-phase liquid immersion cooling; electronic fluoride liquid; thermophysical properties; prediction model; thermal management
# IoT and Blockchain Integration: Applications, Opportunities, and Challenges
## 2 Architecture of IoT
### Sensor Layer  
The sensor layer is composed of sensors and/or actuators. Sensors (or actuators) are low-powered, resource-constrained sensing and acting devices. Figure 2 shows the internal components of a sensor. Each sensor is uniquely identified by its ID or IEEE/MAC addresses. They may be connected with high-powered devices known as a relay or coordinate devices. A network of sensor devices forms a sensor network that may utilize low-power sensor network protocols such as radio-frequency identification (RFID), near field communication (NFC), ZigBee, 802.14, ANT, Bluetooth, among others [16, 17].  
Sensor layers may contain one or more special devices called routers in the gateway layer. Routers help extend network coverage, avoid network obstacles, and provide routes in case of network congestion or device failure. They may be connected to the network coordinator and other routers. However, in a distributed sensor network, there may be more than one network coordinator responsible for adding child nodes and authenticating them. The topology of a sensor network in sensor layers may be (i) star, (ii) mesh, and (iii) hybrid network [19].
## 4 Results
### Calibration and Validation of Microsimulation Model  
The calibration of the model was performed with GEH statistics [43], which is defined by the following equation:  
\[GEH=\sqrt{\frac{2(M-C)^{2}}{M+C}} \tag{1}\]  
where  
\(M\)--hourly traffic from the simulation model;  
\(C\)--real-world hourly traffic collected from the field.  
\(GEH\) statistics facilitate a comparison between two traffic volume sets: real-world volumes and simulation-derived volumes. A GEH value below five indicates a suitable calibration of the simulation model, ensuring that traffic parameters closely reflect real-world conditions. With that, at least 85% of the total traffic volume should have a GEH value lower than five to confirm the model's reliability. On the other hand, if the GEH value exceeds 10, the model is classified as inaccurate, indicating that it does not accurately represent the actual traffic conditions. In this research, GEH values for all approaches of the simulation model were below five, indicating that the model is adequately defined and calibrated. As mentioned earlier, the simulation models were developed using the PTV VISSIM 2024 software, with a duration of 4500 [s]. The first 900 [s] were excluded from the evaluation due to the model's \"warm-up\" period. Additionally, based on the collected and analyzed traffic data, the period from 7:00 to 12:00 a.m. was selected for DLA implementation.  
To ensure the reliability of the simulation results, the model was validated using the empirical data on average travel times collected during a representative weekday that matched the conditions used for demand input. Validation focused on the total time spent on the network by vehicles traveling in both east-to-west and west-to-east directions. For each direction, three controlled field runs were conducted, and the recorded average travel times were compared with the average total time spent (TTS) obtained from ten simulation runs under the baseline (non-DLA) scenario. The TTS indicator served as the key metric for verifying the model's temporal accuracy. In cases where notable deviations were observed, driver behavior parameters such as desired speed, headway, and acceleration profiles were iteratively adjusted to improve alignment with real-world conditions. Additionally, traffic signal plans from the field were analyzed and verified against the modeled settings to ensure consistency in phase sequences, cycle lengths, and coordination. This comprehensive validation process confirmed that the model accurately replicates observed traffic dynamics and provides a reliable foundation for evaluating the impact of dynamic lane assignment strategies.
# Deep-Learning-Based Approach to Detect ICMPv6 Flooding DDoS Attacks on IPv6 Networks
## 4 The Proposed Approach
### Data Prepossessing  
Data preprocessing is the process of converting raw data into an understandable format. It is also a necessary step in data mining because we cannot work with raw data. The dataset published in [28] was used to evaluate the proposed approach's robustness in detecting ICMPv6-based attacks. Table 2 shows the description of the dataset. It is worth mentioning that there is a fundamental difference between IPv4 and IPv6 datasets due to the differences in the packet structure and features [25]. These features form the dataset used to evaluate detection mechanisms. An IPv4 dataset cannot be used to evaluate IPv6 detection mechanisms.  
Figure 3: Phases of the proposed approach.  
Before using ML algorithms, the data should be checked thoroughly by following these steps to ensure the dataset quality:  
**Data cleansing step** cleanses the dataset by identifying incomplete, incorrect, inaccurate, or irrelevant parts of the data for replacement, modification, or deletion. All features with empty values in the dataset are replaced with 0.  
**Data transformation step** changes the data by converting it from one format to another. For example, a unique numeric value replaces the text features in the dataset used. In other words, each feature's unique values are extracted and replaced with a unique number.  
**Data normalization step** transforms data onto the unit sphere or translates data into the range [0, 1] (or any other range). Using Equation (11), a min-max normalization is applied to the feature vector:  
\[x_{i}=\frac{x_{i}-min(x))}{max(x)-min(x)} \tag{11}\]  
**Data balancing step** is a process to ensure the dataset has an equal distribution of classes, i.e., normal and attack. For example, Table 2 shows an unequal distribution of classes (43,446 normal vs. 131,859 attacks); therefore, we overcome this problem by employing the SMOTE [29] oversampling technique to bring the total number of normal classes to 131,859 samples.
# Feasibility of Osseous Landmarks for ACL Reconstruction--A Macroscopic Anatomical Study
## 2 Materials and Methods  
### Specimens  
Thirty-six formaldehyde-phenol-fixed non-paired lower extremities (left = 15 (41.7%), right = 21 (58.3%)) were obtained from voluntary body donations to the Division of Anatomy, Center for Anatomy and Cell Biology of the Medical University of Vienna. Sample size depended on the availability of the specimens and the inclusion criteria. Specimens showing osteophytes extending into the notch, a damaged or nonexistent ACL, or signs of prior surgical procedures at the knee joint were not included.  
The age of the specimens was on average 69.3 \(\pm\) 9.5 years--16 lower extremities originated from male specimens (7 left and 9 right) and 20 lower extremities (8 left, 12 right) originated from female specimens.  
An ethical approval for this study was obtained from the ethics committee of the Medical University of Vienna according to the declaration of Helsinki (EK Nr 1300/2013).  
Figure 1: Schematic drawing of the medial wall of the lateral femoral condyle. The osseous insertion of the anteromedial bundle is depicted in orange, and the osseous insertion of the posterolateral bundle is depicted in green. The lateral intercondylar ridge (LIR) is depicted in blue as anterior border of the ACL, while the lateral bifurcate ridge (LBR) is depicted in red as a border between the two bundles.
## 4 Experiment and Results
### Wavelength Control Beam-Hopping and Fine Steering  
In order to verify beam-hopping in multi-hop bent-pipe LEO satellites, the TLS is employed to change the wavelength across a broad range and superimpose various delay lines on the two subarrays in order to carry out five big angle beam scans, corresponding to \(-40^{\circ}\), \(-20^{\circ}\), \(0^{\circ}\), \(20^{\circ}\), and \(40^{\circ}\) in Figure 10. By comparing the simulation results with the experimental results, there is an experimental beam pointing error of \(<\)0.1\({}^{\circ}\). The pointing error is due to the fact that the high-precision RF phase shifter used still has an accuracy error with a minimum phase shift step of approximately 5.6\({}^{\circ}\) (360/2\({}^{\circ}\)).  
By verifying 10, more precision beam pointing at a large angle is achieved. First, the measured results are calculated to synthesize the antenna far-field pattern as shown in Figure 11 when both subarrays are not supplemented with additional delay, the minimum interval of beam steering is 1.8\({}^{\circ}\) by digital phase shifter for phase compensation only, which achieves precise beam-pointing control. As shown in Figure 12b, combining the aforementioned two adjustment techniques allows for the modification of precise angles across a wide scanning range. Using a digital phase shifter to precision tune the beam, the beam in Figure 12a is scanned to point at \(-37.08^{\circ}\), \(-38.16^{\circ}\), \(-38.88^{\circ}\), \(-39.96^{\circ}\), and \(-40.68^{\circ}\). The beam is first aimed at about \(-40^{\circ}\) by setting the wavelength to 1546.6 nm. In a similar manner illustrated in Figure 12c, alter the wavelength so that the beam points at approximately \(40^{\circ}\), then adjust the digital phase shifter so that the beam also points at \(37.08^{\circ}\), \(37.8^{\circ}\), \(38.16^{\circ}\), \(39.24^{\circ}\), and \(39.96^{\circ}\). Thus, such a two-stage structure enables fast large-angle switching using a wavelength-controlled OTTD while also enabling fine-angle steering and target tracking using a digital phase shifter.  
Figure 10: Far-field pattern of wide-angle steering.
## 2 Background
### Single Ring Geometrical Scattering Channel Model
Finally, \(f_{max}^{P}=V^{P}/\lambda_{c}\) and \(\dot{f}_{max}^{P}(t)=a^{P}(t)/\lambda_{c}\) characterize the maximum Doppler shift caused by the velocity and acceleration of the mobiles, respectively, where \(\lambda_{c}\) stands for the wavelength of the transmitted signal and \(P\) must be replaced by \(R\) or \(T\), which describes the receptor or the transmitter, respectively [6; 17].
## 4 Results
### Experimental Setup
#### 4.1.1 Datasets  
We chose to work with the DAVIS dataset, a widely used video dataset designed for video object segmentation tasks. This dataset encompasses numerous videos, referred to as \"sequences\", with each being accompanied by its corresponding frames. Annotations are available for every frame, including masks for the targeted objects.  
The DAVIS 2017 [51] validation dataset encompasses diverse sequences covering various scenarios, including outdoor, human, sports, dynamic, and static settings. It includes a total of 61 objects, including 24 humans. Each sequence contains at least one object and may involve up to five objects. The length of the videos ranges from a minimum of 47 frames to a maximum of 99 frames. Furthermore, the evaluation within the DAVIS dataset employs well-known metrics, which are denoted as the \(\mathcal{J}\)- and \(\mathcal{F}\)-scores. Further explanations of these evaluation metrics will follow. For the sake of brevity, we will refer to the DAVIS 2017 validation dataset simply as \"DAVIS\".