esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 DPI Applied to Honeynet Traffic and Attacks
### Description of the Architecture
The fields analyzed in this research, in the order presented in Figure 4, are _frame.time_epoch_, _frame.protocols_, _ip.src_, _ip.geoip.src_country_, _ip.geoip.src_city_, _ip.dst_, _udp.srcport_, _udp.dstport_, _tcp.srcport_, _tcp.dstport_, _tcp.flags_, followed by all fields supported by TShark of the application layer protocols Telnet (in the first line, respectively, _telnet.cmd_, _telnet.subcmd_ and _telnet.data_), hl Server Message Block (SMB), Hypertext Transfer Protocol (HTTP), File Transfer Protocol (FTP) and Network Time Protocol (NTP). As the Honeynet traffic comes from the Internet, analysis on layer 2 was not very informative, thus the only inspected field of the data link layer was the _arrival time_, which was analyzed and considered to understand the temporal behavior of the attackers. Detail about the timeline of the collected information in this case is shown in Figure 5.  
Figure 4: Sample lines of the exported csv file. Reticence is used to suppress the repetition of empty fields. Figure 3: Honeynet architecture. [67]. During 141 days of data collected, the average number of packets per day was 81,052 packets per day. Considering this data with previous one collected months before ([70]), it was possible to note that the average number of packets transmitted to the Honeynet has increased significantly. This is a strong indicator that the Honeynet was receiving many more attacks. In the following subsections, more results using DPI regarding data captured and its correspondence with layers of TCP/IP stack are described.
## 3 Results  
The materials used in the manufacture of the thrusters will be first reviewed. For this purpose, the dielectric properties of different plastics (ABS, ASA and EPS) will be compared. Then, its structure, power supply system, measurement and environmental control will be presented. Subsequently, several subsections will include the studies of the electric field carried out by means of FEM analysis, and the obtained values will be compared.  
Table 1 shows the dielectric properties of the plastics used in the construction of the Thrusters.  
Table 1 shows how ABS and especially ASA, that are commonly used in 3D printing, have very high values of dielectric strength ideal for the manufacture of the structure of the thrusters. Although the study was carried out almost entirely using ASA, it was found that for the dimensions and working distances, the use of EPS did not pose any problem.  
Regarding the remaining materials, bare copper wire of 0.25 mm diameter was used in the emitter. A hollow aluminum tube with an outer diameter of 6 mm and an inner diameter of 5 mm was used as collector. All the connections to the power source were made with 16 AWG 30 kV Electric Copper Core Flexible Silicone Wire, sealed with epoxy resin at the points where it was inserted into the frame of the thruster, to avoid any corona effect outside the emitter.  
Figure 2 shows a general view, with the dimensions of the thrusters, in which a single and a dual configuration can be observed, the dimensions and the distance steps between anode and cathode are delimited.  
\begin{table}
\begin{tabular}{c c c} \hline
**Plastic** & **Minimum Dielectric Strength** & **Maximum Dielectric Strength** \\ \hline ABS-Acrylonitrile Butadiene Styrene & 15.7 kV/mm & 34 kV/mm \\ ASA-Acrylonitrile Styrene Acrylate & 40 kV/mm & 105 kV/mm \\ EPS-Expanded Polystyrene & 2 kV/mm & 6 kV/mm \\ \hline \end{tabular}
\end{table}
Table 1: Dielectric properties of plastics.
## 3 Method  
### Preliminaries  
The neural radiation field [7] tries to estimate the state information of all points in a scene to simulate the imaging process. Given a three-dimensional location \(\mathbf{x}\) in a scene and the corresponding direction \(\mathbf{d}\) of the light, the neural radiation field forms a continuous function to predict the volume density \(\sigma\) and radiance \(\mathbf{c}\) as follows:  
\[\mathcal{F}:(\mathbf{x},\mathbf{d})\mapsto(\sigma,\mathbf{c}). \tag{1}\]  
Given that the ray starts from the optical center \(\mathbf{o}\) and the discrete sampling interval is \(t\), these locations \(\mathbf{x}\) can be represented as \(\mathbf{r}(t)=\mathbf{o}+t(\mathbf{d})\), with the number of sampling points on each ray denoted as \(N\). When the volume density and radiance of all ray positions \(\mathbf{x}\) are obtained, we can use the ray-marching algorithm [8] to solve the color \(\mathbf{c}(\mathbf{r})\) rendered on the image as follows:  
\[\mathbf{c}(\mathbf{r})=\sum_{i=1}^{N}T_{i}\alpha_{i}\mathbf{c}_{i}. \tag{2}\]  
The sampling boundary of the light is set from near to far as \([t_{n},t_{f}]\) with \(N\) points, and then the ray locations can be represented as \(\mathbf{x}_{i}=\mathbf{o}+t_{i}(\mathbf{d})\). In Equation (2), \(T_{i}\) refers to the transmittance of each point by superimposing the opacity \(\alpha\) of the \(i-1\) points in front of \(\mathbf{x}_{i}\), with the opacity \(\alpha\) of each point being calculated from the volume density \(\sigma\):  
\[\alpha_{i}=1-\exp(-\sigma_{i}\delta_{i})\quad\text{and}\quad T_{i}=\prod_{j=1 }^{i-1}(1-\alpha_{j}), \tag{3}\]  
where \(\delta_{i}=t_{i+1}-t_{i}\) is the spatial distance between adjacent samples. NeRF uses multilayer perceptron (MLP) to estimate the most difficult volume density \(\sigma\) and the radiance \(\mathbf{c}_{i}\) of \(\mathbf{x}_{i}\); thus, the RGB value, \(\mathbf{c}(\mathbf{r})\), can be calculated using the ray-marching algorithm described above. If \(\mathcal{R}\) is denoted as a batch of randomly selected rays during training, the model is optimized by calculating the error between the rendered color \(\mathbf{c}(\mathbf{r})\) and the ground image pixel color \(\mathbf{c}_{GT}(\mathbf{r})\):  
\[\sum_{\mathbf{r}\in\mathcal{R}}\left\|\mathbf{c}(\mathbf{r})-\mathbf{c}_{GT}( \mathbf{r})\right\|_{2}^{2}. \tag{4}\]
## 5 Performance Evaluation  
### Simulation Settings  
#### 5.1.1 Topology Settings  
As a consequence of the heterogeneous and stochastic placement characteristics of IoT equipment, the IoT-MEC network's actual topology is irregular. To address this, we employ the NetworkX3.1 [40] library to construct three representative network topologies for simulation purposes in our study. The utilization of this approach facilitates the demonstration of the suitability and universality of the MRL-SFCP algorithm. Within the scope of this investigation, the network topologies include a random network (RN), small-world network (SWN) [41], and scale-free network (SFN) [42], correspondingly. Amongst each category of network topology, a total of 24 nodes exist, of which 8 nodes are designated as cloudlets, which are primarily utilized to execute IoT-SRs by employing placed VNFs. Additionally, the RN has a connectivity probability of 0.4, the SWN has 10 neighbor nodes and a reconnect probability of 0.2, and the SFN consists of an initial node count of 7.  
We consider six types of VNFs: firewall, proxy, NAT, IPS, IDS, and gateway, with CPU requirements of 1600 MIPS, 1200 MIPS, 2000 MIPS, 3000 MIPS, 2400 MIPS, and 1800 MIPS, respectively [2]. Each cloudlet is placed with one or two types of VNF instances, and IoT-SRs are randomly produced in AP nodes. We set the CPU capacity of each cloudlet within the range of 3000 to 8000 MIPS. The bandwidth and delay of each link are set within the ranges of [1000, 10,000] Mbps and [2; 5] ms, respectively. For the sake of simplifying the problem, the delay on cloudlets is randomly set from 4 to 8 ms. We set the weighted factors in Equation (19), i.e., \(\lambda_{1}\) and \(\lambda_{2}\), as 3 and 1, respectively.
# A System to Improve Port Navigation Safety and Its Use in Italian Harbours
## 2 Materials and Methods
### Forecasting Sea Level in Harbours
\begin{table}
\begin{tabular}{c c c c c} \hline
**Start** & **End** & **\(\Delta\)p [hPa]** & **\(\Delta\)h [cm]** & **\(J_{\rm ph}\) [cm\(\cdot\)hPa\({}^{-1}\)]** \\ \hline
07/09/2010 & 16/09/2010 & 13.1 & 23.5 & 1.8 \\
18/06/2011 & 23/06/2011 & 9.7 & 16.5 & 1.7 \\
13/07/2012 & 20/07/2012 & 10.4 & 20.9 & 2 \\
12/08/2014 & 19/08/2014 & 8.9 & 15.7 & 1.8 \\
06/06/2015 & 15/06/2015 & 9 & 18.9 & 2.1 \\
17/07/2016 & 25/07/2016 & 7.6 & 14.8 & 1.9 \\
08/08/2017 & 18/08/2017 & 7.9 & 16.1 & 2 \\
17/07/2018 & 23/07/2018 & 4.9 & 12.4 & 2.5 \\
15/12/2018 & 20/12/2018 & 14.1 & 23.3 & 1.7 \\
27/11/2019 & 13/12/2019 & 19.5 & 45.2 & 2.3 \\
20/05/2021 & 26/05/2021 & 7.4 & 14 & 1.9 \\ \hline \end{tabular}
\end{table} 
Table 1: Significant events occurred since 2010 in the port of Livorno. \begin{table}
\begin{tabular}{c c c c c} \hline
**Start** & **End** & **\(\Delta\)p [hPa]** & **\(\Delta\)h [cm]** & **\(J_{\rm ph}\) [cm\(\cdot\)hPa\({}^{-1}\)]** \\ \hline
30/08/2010 & 09/09/2010 & 10.9 & 19.3 & 1.8 \\
15/05/2011 & 23/05/2011 & 8.1 & 16.8 & 2.1 \\
23/05/2011 & 02/06/2011 & 8.1 & 16.3 & 2 \\
21/11/2011 & 03/12/2011 & 11.1 & 21.7 & 2 \\
10/05/2012 & 18/05/2012 & 14.9 & 27 & 1.8 \\
23/12/2012 & 30/12/2012 & 9.1 & 16.2 & 1.8 \\
13/05/2014 & 25/05/2014 & 12.3 & 29 & 2.4 \\
08/02/2015 & 22/02/2015 & 25.2 & 55.5 & 2.2 \\
05/06/2015 & 20/06/2015 & 10 & 22.1 & 2.2 \\
01/05/2019 & 08/05/2019 & 15.1 & 35.3 & 2.3 \\
20/06/2020 & 30/06/2020 & 15.7 & 7.3 & 2.2 \\ \hline \end{tabular}
\end{table} 
Table 2: Significant events occurred since 2010 in the port of Bari. in coastal areas: a change of atmospheric pressure can be converted, through J\({}_{\rm ph}\), into a forecasted meteorological tide and therefore in an expected sea level, and finally in a new bathymetric map. Finally, it should be noted that it is appropriate to update continuously the estimate of J\({}_{\rm ph}\) for a certain basin, in particular after events that change the topography (e.g., dredging operations, coastal erosion, sediment deposit, sea bottom subsidence). In any case, multi-decade statistics of J\({}_{\rm ph}\) is needed if we want to evaluate the possible effect of long-term phenomena such as climate change on the hydrobarometric transfer factor.
# Detection and Localisation of Abnormal Parathyroid Glands: An Explainable Deep Learning Approach
## 3 Results
### Results Are from Surgically Confirmed Cases  
Detailed information from operated cases was available in 472 patients treated surgically at our hospital. According to surgical and histopathological findings, 372 patients had solitary parathyroid neoplasms (369 adenomas and three carcinomas), whereas 97 patients (17 with secondary or tertiary and 80 with primary HPPT) carried two or more abnormal PGs. In three cases, no abnormal PG could be identified during surgery. In four patients, the parathyroid adenoma was located in the mediastinum. These cases were excluded from further evaluation because, with a pinhole collimator, only a small or no part of the mediastinum was included in the imaging field of planar scintigraphy. The results of Nuclear Medicine experts' diagnosis and the ParaNet model in 465 patients with positive surgical findings in the neck are listed in Table 9. Only the sensitivity and the false positive rate of tested methods could be examined in this group.  
On a patient basis, the sensitivity of the ParaNet model is close to that of medical experts. However, concerning abnormal PGs, ParaNet sensitivity is falling behind experts by about 10% in both solitary neoplasm and multi-glandular disease subgroups. Similar to the findings in the study cohort of 632 patients, the ParaNet's false positive rate in the operated group is also high.
## 3 Results
### SICL Incorporating the Proposed Design Methodology  
#### 3.1.1 Loss Reduction  
Interconnect loss can be assessed using the attenuation constant. As illustrated in Figure 6, the introduction of air cavities and a nonuniform-width inner conductor yields a 9.76% reduction in the attenuation constant at 100 GHz compared to the traditional SICL, attributed to the lower equivalent loss tangent. Additionally, when compared to the SICL with air cavities, the proposed design demonstrates a 5.76% decrease in the attenuation constant, primarily due to improvements in impedance matching. Notably, this improvement becomes more pronounced as the frequency increases.
###### Abstract  
During unmanned drilling operations, the velocity of drill cuttings serves as an important indicator of drilling conditions, which necessitates real-time and accurate measurements. To address challenges such as the small size of cuttings, weak feature representations, and complex motion trajectories, we propose a novel velocity measurement method integrating small-object detection and tracking. Specifically, we enhance the multi-scale feature fusion capability of the YOLOv11 detection head by incorporating a lightweight feature extraction module, Ghost Conv, and a feature-aligned fusion module, FA-Concat, resulting in an improved model named YOLOv11-Dd (drilling debris). Furthermore, considering the robustness of the ByteTrack algorithm in retaining low-confidence targets and handling occlusions, we integrate ByteTrack into the tracking phase to enhance tracking stability. A velocity estimation module is introduced to achieve high-precision measurement by mapping the pixel displacement of detection box centers across consecutive frames to physical space. To facilitate model training and performance evaluation, we establish a drill-cutting splash simulation dataset comprising 3787 images, covering a diverse range of ejection angles, velocities, and material types. The experimental results show that the YOLOv11-Dd model achieves a 4.65% improvement in mAP@80 over YOLOv11, reaching 76.04%. For mAP@75-95, it improves by 0.79%, reaching 41.73%. The proposed velocity estimation method achieves an average accuracy of 92.12% in speed measurement tasks, representing a 0.42% improvement compared to the original YOLOv11.  
drilling debris velocity measurement; object detection; multi-object tracking; real-time monitoring; residual convolutional layer chain +
Footnote †: journal: Applied sciences  
Article
# Two-Way Multi-Antenna Relaying with Simultaneous Wireless Information and Power Transfer
## 4 Test Protocol
### TS-DF Scheme  
In the MA phase, the received RF signal at the relay node R is the same as Equation (2) except that \(P_{1}=\zeta_{1}P^{\prime}\), \(P_{2}=\zeta_{2}P^{\prime}\), and the harvested energy during the energy harvesting time is given by:  
\[E_{m}=\frac{2\alpha\eta N\big{(}P\sigma^{2}+(1+\alpha)\sigma_{\rm a}^{2}\big{)} }{1+\alpha},\ m=1,2,...,M. \tag{29}\]  
After RF-band-to-baseband signal conversion at the information receiver as shown in Figure 3b, the sampled baseband signal vector is given by:  
\[\widetilde{\mathbf{y}}_{\rm r} =\left[\begin{array}{c}\sqrt{P_{1}}h_{1,1}x_{1}+\sqrt{P_{2}}h_ {2,1}x_{1}+n_{\rm r,1}^{\rm[a]}+n_{\rm r,1}^{\rm[c]}\\ \sqrt{P_{1}}h_{1,2}x_{1}+\sqrt{P_{2}}h_{2,2}x_{2}+n_{\rm r,2}^{\rm[a]}+n_{\rm r,2}^{\rm[c]}\\ \vdots\\ \sqrt{P_{1}}h_{1,M}x_{1}+\sqrt{P_{2}}h_{2,M}x_{2}+n_{\rm r,M}^{\rm[a]}+n_{\rm r,M}^{\rm[c]}\end{array}\right]\] \[=\left[\begin{array}{c}\sqrt{P_{1}}h_{1,1}\sqrt{P_{2}}h_{2,1}\\ \sqrt{P_{1}}h_{1,2}\sqrt{P_{2}}h_{2,2}\\ \vdots\vdots\\ \sqrt{P_{1}}h_{1,M}\sqrt{P_{2}}h_{2,M}\end{array}\right]\left[\begin{array}{ c}x_{1}\\ x_{2}\end{array}\right]+\left[\begin{array}{c}n_{\rm r,1}^{\rm[a]}\\ n_{\rm r,2}^{\rm[a]}\\ \vdots\\ n_{\rm r,M}^{\rm[a]}\end{array}\right]+\left[\begin{array}{c}n_{\rm r,2}^{ \rm[c]}\\ n_{\rm r,2}^{\rm[c]}\\ \vdots\\ n_{\rm r,M}^{\rm[c]}\end{array}\right] \tag{30}\] \[=\left[\begin{array}{c}\sqrt{P_{1}}\mathbf{h}_{1}\sqrt{P_{2}} \mathbf{h}_{2}\end{array}\right]\left[\begin{array}{c}x_{1}\\ x_{2}\end{array}\right]+\left(\mathbf{n}_{\rm r}^{\rm[a]}+\mathbf{n}_{\rm r}^{ \rm[c]}\right)\] \[=\Xi\left[\begin{array}{c}x_{1}\\ x_{2}\end{array}\right]+\mathbf{v}_{\rm r}\]  
where \(n_{\rm r,m}^{\rm[c]}\sim CN\big{(}0,\sigma_{\rm c}^{2}\big{)}\) is the AWGN due to such conversion. Assuming that \(P_{1}\), \(P_{2}\), \(\left\{h_{1,m}\right\}_{m=1}^{M}\), and \(\left\{h_{2,m}\right\}_{m=1}^{M}\) are known at the relay node and applying ZF detection, estimates of the information signals \(x_{1}\) and \(x_{2}\), denoted by \(\hat{x}_{1}\) and \(\hat{x}_{2}\). respectively, are obtained as:  
\[\left[\begin{array}{c}\hat{x}_{1}\\ \hat{x}_{2}\end{array}\right]=\left(\Xi^{\rm H}\Xi\right)^{-1}\Xi\;\widetilde{ \mathbf{y}}_{\rm r}. \tag{31}\]  
Figure 3: (**a**) Key parameters in the TSR protocol for energy harvesting and information processing at the relay node; and (**b**) Block diagram of the relay receiver (with a focus on its \(m\)-th antenna) in the TSR protocol.
## 2 Main Results
### Hybrid PID Controller with Smooth Transition to the Setpoint  
Clearly, the amplitude of the control signal directly depends on the magnitudes of \(e(t)\), \(\int_{0}^{t}e(t)d\tau\) and \(\frac{de(t)}{dt}\) at each instant \(t\). Therefore, the proposed approach attempts to keep these terms as small as possible to yield the smallest possible \(u(t)\) for all \(t\) while achieving the control goal.  
To achieve this, the control scheme shown in Figure 1 is modified as illustrated in Figure 2, where the PID block consists of the components enclosed within the dotted line in Figure 1.  
Figure 1: Closed-loop system with a classical PID controller scheme.  
The difference between the control schemes illustrated in Figures 1 and 2 lies in the error signals, \(e(t)\), used by the PID controllers, which differ significantly. In a classical (typical) PID controller scheme, the error is simply defined as the difference between the setpoint and the variable to be regulated. Instead, the proposed approach incorporates an additional exponential component in the error signal, with its parameters determined by a hybrid system. These two features give the PID controller its adaptive behavior. A detailed explanation of this new control algorithm is given below.
## 5 Engineering Applications of Lattice Structures Built with Polygon Hollow Shapes
### Applications in the Automotive Industries  
To reduce the overall weight while still maintaining the structural integrity, lattice structures composed of polygon hollow shapes are used in the design of chassis and frames of vehicles [17]. As a result, the fuel efficiency and overall performance are improved. In addition, lightweight polygon structures are built into body panels to maintain the structural integrity while lowering the weight of vehicles [8; 10; 60]. During collisions, lattice structures with polygonal hollow shapes are meant to absorb and disperse the impact energy more effectively. This enhances safety in vehicles by reducing the magnitude of force transmitted to passengers. Bumpers with lattice structures offer impact resistance, as well as the absorption of energy, thus increasing safety in collisions at low speeds [53; 60; 61]. The geometric shapes of lattice structures are chosen to allow for a good absorption of energy during crashes, and such structures are best suited for parts that receive impact, such as front and rear crash zones [17; 53; 61]. Integrating polygon hollow structures into particular regions of a vehicle, such as crash boxes, assists in controlling and distributing energy during a collision, thus leading to an enhanced crashworthiness [17; 31; 61].  
Seat frames are designed with lattice structures, allowing for a balance of strength and reduction in weight. This improves the fuel efficiency and overall performance of vehicles [62]. Integrating lattice structures into interior parts, including dashboards and trim panels, also assists in reducing the weight of non-structural parts while preserving the safety or aesthetic appeal [63]. In the design and manufacture of control arms for suspension systems, lattice structures offer the required stiffness and strength while maintaining the weight at a minimum [17; 62]. Furthermore, lightweight lattice structures are typically used in the design of springs, reducing unsprung material, and optimising the control of vehicles and comfort of the passenger [62].  
Lattice structures are useful for the design of heat-dissipating parts, such as engine mountings and heat shields, where an efficient heat transfer is crucial for maintaining ideal operating temperatures [62; 64]. Lattice structures are used in the design of grilles and air intakes as well, to preserve the structural integrity whilst optimising the aerodynamics, reducing the drag, and improving the fuel efficiency [62]. The incorporation of lattice parts within designs for wheel rims reduces the weight whilst maintaining the strength, improving the performance and control in vehicles [17]. In order to integrate considerations of weight with structural requirements, lattice structures are also used in the design of parts within electric vehicle (EV) battery packs [65].
## 3 Genetic Algorithm for VNF-Mapping in Cloud/MEC RAN-Based 5G Architectures
### Reconfiguring VNF Provisioning with GASM  
GASM provides a VNF-enabled network configuration well fitted to solve the service requests coming from a certain number of users, estimated as an average value. However, this kind of network design is not adapted to the time-varying traffic, therefore causing inefficient usage of IT resources. Consequently, a rise of the service blocking ratio may appear when the number of users is higher than the average. However, a decrease in the number of users may cause the over-consumption of IT resources, leading to a higher energy consumption.  
In order to increase the performance of the network in a dynamic scenario, the best alternative is to reconfigure the VNF mapping. When a reconfiguring scenario is considered, the time is divided into time slots and the VNF provisioning algorithms are launched at the beginning of each slot. In order to predict the traffic in the following slot, we have considered a very simple method, shown in Equation (1):  
\[T_{j+1}=T_{j}+\alpha\big{(}T_{j}-T_{j-1}\big{)}, \tag{1}\]  
where \(T_{j+1}\) represents the number of users at the next time slot (i.e., the estimation), \(T_{j}\) represents the current slot total number of users and \(T_{j-1}\) represents the total number of users at the previous temporal slot. The scale factor \(\alpha\) is the maximum variation in the number of users (measured with traffic monitors) from one time slot to the next one, typically considering only a window of time, i.e., a day or a week. Despite its simplicity, this basic estimation method provides satisfactory results as will be shown in the following section. Once the traffic for the following time slot is estimated, GASM uses that information to determine a VNF provisioning and establish the new mapping in the IT resources of the MEC and CO.