esa-sceva/satcom-chunk-collection huggingface dataset:
# The Design and Deployment of an End-To-End IoT Infrastructure for the Natural Environment
## 4 Design and Deployment
### Node Technology  
As mentioned above, we use two types of sensing nodes--soil sensing and livestock tracker nodes. The soil sensing nodes are used to take measurements related to the soil, whilst the livestock trackers are used to monitor the location of farm animals (specifically sheep during this project) in the area concerned. Both types of nodes have different physical requirements but need to be able to communicate with one another and so have a common link technology. In addition to this, we use a long-range link node and a Raspberry Pi.  
In this section, we present and justify our design choices in building these nodes, comparing with alternative existing technologies where available. Our overriding motivation was to develop a platform that was cheap and flexible. Images of each node type are shown in Figure 4.
# Algorithm for Generating 3D Geometric Representation Based on Indoor Point Cloud Data
## 1 Introduction  
With the advancement of three-dimensional (3D) scanning equipment, diverse studies associated with reverse engineering using point cloud data (PCD) are being conducted at present. In particular, methods to generate geometric representation from data captured by porTable 3D scanners have been developed mainly in the architecture, engineering, and construction (AEC) industries [1; 2; 3]. Compared to an outdoor environment that can be scanned by a navigation system-based 3D scanning, an indoor environment has limitations in utilizing the system (e.g., global navigation satellite system, inertial navigation system) [4]. Thus, the 3D geometric representation of the indoor environment is generated by manual operation due to a lack of essential information, such as a normal vector and the scanner's trajectory.  
However, the manual operation requires a time-consuming process and causes an inconsistent accuracy of geometric representation. The time-consuming process reduces the efficiency of reverse engineering because an executor who reconstructs the building components (e.g., structural member, non-structural member, furniture) from the raw PCD checks the result of geometric representation iteratively to minimize an error [5; 6; 7]. In addition, the accuracy of geometric representation depends on the ability of the executor. If the executor has little experience, the accuracy of the result will decreasesince the indoor PCD capture through the porTable 3D scanning method lacks the information for detecting the building components [8; 9; 10].  
To compensate for these limitations, this study proposes a new algorithm capable of generating 3D geometric representation using the topological feature of planar interior objects to detect the main structural members such as walls, floor, and ceiling [11]. This study consists of four parts. First, previous studies in the relevant field are reviewed in order to identify limitations in the current method for generating 3D geometric representation. Second, the framework of the new algorithm is presented. Third, the 3D geometric representation is illustrated by the refinement and processing of indoor PCD through a case study. Finally, the error rate of the geometric representations of the main structural members of the building is analyzed to validate the results of this study. The results of this study are expected to be used for the generation of building information modeling (BIM) from PCD that can be used to identify differences in design and construction.
## 3 Discussion of the Noise Results
### Effect of the Pore Position  
Figure 13 shows \(\Delta\)SPL as a function of free-stream velocity and frequency for structured porous trailing edges when the last downstream subregion, the middle subregion, and the upstream subregion are covered by 0.6 mm pores, from left to right, respectively. Figure 14 plots the corresponding \(\Delta\)OASPL. Similar to the observation in Section 3.3, noise reduction is slightly different for a zero angle of attack of \(\alpha=0^{\circ}\) and a non-zero angle of attack of \(\alpha=10^{\circ}\). Generally, larger OASPL reduction is observed for L00M00N06 at \(\alpha=0^{\circ}\)  
Figure 11: \(\Delta\)SPL as a function of free-stream velocity and frequency for structured porous trailing edges with different pore coverages: (**a**) L06M06N06 with \(\alpha=0^{\circ}\), (**b**) L00M06N06 with \(\alpha=0^{\circ}\), (**c**) L00M00N06 with \(\alpha=0^{\circ}\), (**d**) L06M06N06 with \(\alpha=10^{\circ}\), (**e**) L00M06N06 with \(\alpha=10^{\circ}\), and (**f**) L00M00N06 with \(\alpha=10^{\circ}\).  
while greater noise reduction is obtained for L06M00N00 at \(\alpha=10^{\circ}\). Both [PERSON] et al. [31] and [PERSON] et al. [32] have shown that vortex shedding-related noise will radiate at the solid-to-porous junction section, since a blunt edge occurs in this area due to the pores. [PERSON] and [PERSON] [17] further demonstrated that the level of this unwanted noise increases with the thickness of the blunt edge at \(\alpha=0^{\circ}\). Since both L06M00N00 and L00M06N00 have a larger thickness of the blunt edge induced by the pores, additional bluntness-related noise will jeopardize the overall performance, and thus their \(\Delta\)OASPL levels are less than L00M00N06 at \(\alpha=0^{\circ}\). Moreover, [PERSON] and [PERSON] [33] have illustrated that a porous permeable structure located in the middle of the model will generate a cavity tonal component under symmetrical flow conditions, which can also explain the better noise reduction in L00M00N06 at a zero angle of attack. On the other hand, [PERSON] et al. [24] have revealed that the laminar separation bubble starts earlier on the suction side and moves downstream on the pressure side with an increasing angle of attack. Therefore, the airflow passing through pores that are distributed in the upstream subregion will have an earlier impact on the boundary layer regime and then suppress laminar instability tonal noise, which can explain the better performance of L06M00N00 at \(\alpha=10^{\circ}\).  
Figure 13: \(\Delta\)SPL as a function of free-stream velocity and frequency for structured porous trailing edges with different pore positions: (**a**) L00M00N06 with \(\alpha=0^{\circ}\), (**b**) L00M06N00 with \(\alpha=0^{\circ}\), (**c**) L06M00N00 with \(\alpha=0^{\circ}\), (**d**) L00M00N06 with \(\alpha=10^{\circ}\), (**e**) L00M06N00 with \(\alpha=10^{\circ}\), and (**f**) L06M00N0 with \(\alpha=10^{\circ}\).
## Methodology
### PPP Model and Data Processing Strategy
Afterward, these stacked residuals are low-pass filtered with a cutoff frequency of 10 s to generate multipath corrections since any prominent multipath over shorter periods are not anticipated (34). Finally, the phase observations on the day of interest are corrected, and then processed by the forward Kalman filter for simulating the kinematic PPP in real time.
# Design and Evaluation of Steganographic Channels in Fifth-Generation New Radio +
## 4 Protocol Analysis and Covert Channel Design
### Analysis of Cover Timing Patterns  
For completeness, the susceptibility of the PDCP to timing patterns is analyzed below. Network-specific pattern collection [22] is also used here. However, the focus of the analysis is only on the protocol-aware patterns, as the protocol-agnostic patterns can always be applied without relying on a specific protocol.
## 4 Results and Discussion
### Parameter Optimization
#### 4.2.4 Optimization of the Number of Base Blocks  
After setting these parameters, we compared four different numbers of base blocks, that is, 2, 3, 4, and 5, to investigate their effects on this experiment. The classification performance results are listed in Table 3.  
As shown in Table 3, the model results increased as the number of base blocks increased. The number of memory items and total parameters of the generated model increased at double or multiple rates. When the number of base blocks was four or five,  
Figure 12: Learning rates comparison.  
Figure 13: Batch size comparison.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Base Block** & **2** & **3** & **4** & **5** \\ \hline OA (\%) & 98.188 & 98.912 & 99.216 & 99.342 \\ Train time (s) & 215 & 277 & 367 & 791 \\ Memory (MB) & 1.98 & 5.22 & 16.30 & 59.10 \\ Model params (MB) & 0.28 & 1.18 & 4.73 & 18.82 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison of the number of base blocks.  
the overall classification accuracy of the model was high, with the latter increasing by 0.126% compared with the former. However, the former only accounted for 46.40% and 27.58% of the latter in terms of the training time and memory used by the generated model, respectively. Therefore, we selected four base blocks as inputs for the model structure.
# A Distributed Indoor Mapping Method Based on Control-Network-Aided SLAM: Scheme and Analysis
## 4 Discussion  
On the basis of IMU-aided SLAM, the further introduction of control network information can effectively improve the relative accuracy of mapping. The control point information can introduce absolute correction information into the SLAM process, which can improve the relative accuracy of SLAM. At the same time, the introduction of control point information into the SLAM back end can be used to convert the mapping results to the global frame where the control point coordinates are located, so that large-scale mapping can be divided into multiple small mapping areas. The absolute accuracy was calculated by comparing the feature points of the mapping result and the reference map  
Figure 8: Distributed mapping result (MAP_VII).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Result**} & \multirow{2}{*}{**Number of Blocks**} & **Number of Control Points** & **Number of Common Feature Points** & **RMS of Position Error** \\ \hline MAP\_VII & 4 & 35 & 28 & 0.14 m \\ \hline \hline \end{tabular}
\end{table}
Table 3: Stitching accuracy of distributed mapping results.  
and reached 0.13 m, which is similar to the stitching accuracy in the distributed mapping process. Existing distributed mapping methods can also perform distributed mapping well, but they require relative matching between maps to achieve map stitching. When the relative matching is accurate, its accuracy may be better than that of our scheme. Our proposed scheme is a simplified scheme which can omit the time-consuming process of relative matching to improve efficiency with a limited loss of accuracy and obtain viable distributed mapping results. The results obtained by our scheme can also provide a good prior value for relative map matching.
## 5 Conclusions  
Researchers are passionate to try different types of classifiers and build new models with an effort to enhance the accuracy of diabetes prediction. In this paper, the same vision was followed to reach high prediction accuracy. All the Machine Learning (ML) and Deep Learning (DL) classifiers that have been used in the last six years were reviewed regarding their frequency of use and accuracy. ML classifiers with one or zero frequency have been implemented on the PID dataset to set recommendations regarding their usage. The obtained accuracy by these ML techniques was 68%-74%. For the DL algorithms, the highest accuracy achieved by researchers was 95%. As a future work, the non-used classifiers can be applied to other datasets in a combined model to enhance further the accuracy of predicting the Diabetes disease. Conceptualization, S.L.-M.-S, L.A. and R.A., T.S. ; methodology, L.A. and R.A.; software, L.A. and R.A.; validation, S.L.-M.-S, L.A. and R.A.; formal analysis L.A. and R.A.; investigation, S.L.-M.-S, L.A. and R.A.; resources, L.A. and R.A.; writing--original draft preparation L.A. and R.A.; writing--review and editing, S.L.-M.-S., T.S. ; visualization, L.A. and R.A.; supervision, S.L.-M.-S.; project administration, T.S. ; funding acquisition, T.S. This research received no external funding. This work was supported by the Artificial Intelligence and Data Analytics Lab (AIDA), Prince Sultan University, Riyadh, Saudi Arabia. The authors declare no conflict of interest. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Dataset** & **Reference** & **Number of Samples** & **Number of Features** & **Features** & **Link** \\ \hline \hline \multirow{4}{*}{ \begin{tabular}{} \end{tabular}  } & \multirow{4}{*}{[4]} & \multirow{4}{*}{-} & \multirow{4}{*}{8} & \begin{tabular}{} \end{tabular}  & \ \begin{tabular}{} \end{tabular}  & \begin{tabular}{} \end{tabular}  \\  & & & & & \\ \cline{1-
## 5 Numerical Results
### Random \(L_{k}\) and \(P_{a}=0.1\%\)  
We consider in this subsection a random number of packets sent by each device following \(L_{k}\sim\mathcal{U}[1,10]\), and a random number of devices being activated at each frame following a binomial distribution with an activation probability of \(P_{a}=0.1\%\), such that \(K_{a}\leq K\). The number of available RA pilots is also reduced to \(\tau_{p}=40\) in order to keep the simulation time short. The results presented in Figures 10 and 11 are generated with 64000 Monte-Carlo realizations. Figure 10 presents the average per-device throughput and Figure 11 the average network throughput. The reward in the QL framework of the mQmMTC GF RA protocol is calculated using (19) while assuming that \(\mathbb{E}[L_{k}]=5.5\) is known at the devices' side. In terms of both performance metrics, our proposed mQmMTC protocol remains quite close to that of the ideal cQmMTC protocol, while always superior to that of Baseline and iQmMTC. While the per-device throughput of Baseline drops below 0.5 for \(K\approx 2400\), this happens with \(K\approx 2800\) for iQmMTC, with \(K\approx 3100\) for mQmMTC, and with \(K\approx 3400\) for the ideal cQmMTC. Similarly, the network throughput falling point in Figure 11 occurs with \(K\approx 2300\) for the Baseline, with \(K\approx 2400\) for iQmMTC, with \(K\approx 2800\) for mQmMTC, and with \(K\approx 3100\) for the ideal cQmMTC. These results corroborate the feasibility of the proposed mQmMTC GF RA protocol to address the challenges of massive machine-type communications in the framework of next-generation massive multiple access systems.
# Ensemble Classifiers for Network Intrusion Detection Using a Novel Network Attack Dataset +
## 2 Background
### Machine Learning  
ML is the field of computer science that studies algorithms that automatically improve during training and experience, enabling a computer to make accurate predictions when fed data withoutbeing explicitly programmed [16]. ML algorithms use a subset of a larger dataset, known as training data or sample data, to build a mathematical model to make predictions or decisions based on a given problem. ML is applied in a wide variety of domains--including spam filtering, Internet search engines, recommendation systems, voice recognition, and computer vision--where conventional algorithms cannot accomplish the required tasks. Similarly, in the field of cybersecurity, several ML methods have been proposed to monitor and analyze network traffic to recognize different anomalies. Most of these methods identify anomalies by looking for deviations from a basic regular traffic model. Usually, these models are trained on a set of attack-free traffic data that are collected over a long time period.
# The Impacts of Open Data and eXplainable AI on Real Estate Price Predictions in Smart Cities
## 3 Results  
This section presents the results obtained from a comprehensive three-phase analysis focused on predicting real estate prices using an XGBoost model. Initially, we provide a concise yet comprehensive comparative model analysis overview, justifying the XGBoost model selection based on empirical evidence in alignment with the study's predictive accuracy and model interpretability objectives. Next, the best-performing model, the Baseline Model--for proprietary data, is expanded into an Open Data Model by incorporating open data features and then retrained, allowing for a detailed examination of its impact on predictive precision and providing the most complete and performant solution. Subsequently, we explore SHAP values analysis to interpret the predictions of the Open Data Model, offering deep insights into the importance of features and interactions.  
In this section, we present the results produced using Python (version 3.10.12), employing a variety of libraries offering an extensive array of capabilities. We utilized pandas (version 1.5.3), numpy (version 1.23.5), and scikit-learn (version 1.2.2) for data manipulation and analysis. Machine learning tasks were executed using xgboost (version 2.0.1) and tensorflow (version 2.15.0). We integrated optuna (version 3.4.0) and scipy (version 1.11.3) for optimization purposes. Visualization tasks were accomplished with the assistance of seaborn (version 0.12.2), matplotlib (version 3.7.1), plotly (version 5.15.0), and kaleido (version 0.2.1). The utility library joblib (version 1.3.2) was used for intermediate storage and loading, while platform-specific operations were managed by the google.colab library (version 1.0.0). We incorporated shap (version 0.44.0) for in-depth model explanation and interpretability. All these workflows were executed in the cloud-based runtime environment of Google Colab ([[https://colab.research.google.com/](https://colab.research.google.com/)]([https://colab.research.google.com/](https://colab.research.google.com/)) (accessed on 11 January 2024)), leveraging VMs equipped with Nvidia's T4 GPU hardware acceleration and high-RAM capabilities.
# In Vivo Efficacy of Contact Lens Drug-Delivery Systems in Glaucoma Management. A Systematic Review
## 3 Results
### CL with Implant  
[PERSON] et al. proposed to include drug implants in the outer periphery of a silicone CL [41; 42]. This technique prevented the limiting of vision through the CL centre. Each implant was loaded with bimatoprost, timolol, or hyaluronic acid. Hylauronic acid improved comfort during the extended CL wear (Figure 5).  
In the first study on rabbit tears, the CL with timolol and hyaluronic acid implants was compared to the timolol-soaked CL and timolol eyedrops [41]. Eyedrops reduced IOP by 2.24 mmHg on average, returning to baseline after 12 h. The soaked CL and the CL with implants lowered IOP by 2.95 mmHg and 3.19 mmHg on average, and IOP recovered after 96 h and 144 h, respectively. Both the soaked CL and the CL with implant showed an initial burst drug release. It was interesting to note that the CL with implant obtained this achievement with a drug dose of 154 \(\upmu\)g, although a single eyedrop contained 250 \(\upmu\)g [41].  
In the following study, a CL with bimatoprost-timolol-hyduronic acid implants was compared with soaked a bimatoprost-timolol CL and bimatoprost-timolol eyedrops in glaucomatous rabbits [42]. Similar results to the previous study were obtained, bearing in mind that prostaglandin does not affect rabbits IOP. Eyedrops reduced IOP by 3.10 mmHg at maximum, returning to baseline after 12 h. The soaked CL and the CL with implants lowered IOP at maximum by 4.33 mmHg and 5.00 mmHg and kept IOP below baseline values for 96 h and 120 h, respectively. The CL with implants showed a sustained drug release in tears (17.26 h) compared to the soaked CL (6.61 h) and eyedrops (11.47 min). Interestingly, the soaked CL and the CL with the implants showed a reduced initial burst of 26.18- and 77.11-fold, respectively, compared to the eyedrops. No signs of ocular irritation were reported [42].
## 5 Experimental Results and Discussion  
In this section, we provide a qualitative and quantitative analysis of the proposed word-based SISO semisystolic multiplier-squarer structure and the efficient word-based serial multiplier structures available in the literature [45; 50; 60; 61]. The qualitative analysis concentrates on developing analytical formulas for both the area and time complexities of the compared multiplier structures. On the other hand, the quantitative analysis focuses on providing the ASIC implementation results for all the compared designs to confirm the qualitative findings.
## 4 Discussion
Furthermore, post-implementation simulations have been carried out on both the encryption and decryption systems individually, so obtaining the total on-chip power consumption equal to 1.17 W and 0.99 W with the chip temperature equal to 26.5 \({}^{\circ}\)C and 26.1 \({}^{\circ}\)C, respectively. By providing the plaintext data packets in input to the encryption block every 40.86 ns, the post-implementation simulation on the combined encryption/decryption system indicates a power consumption of only 365 mW, with a 25.5 \({}^{\circ}\)C chip temperature.
# Multi-Path Routing Algorithm Based on Deep Reinforcement Learning for SDN
## 5 Results and Validation
### Performance Comparison
#### 5.3.2 Network Topologies  
These experiments were performed in two distinct network topologies: NSFNet-14 [57] and Germany-50 [58]. NSFNet-14 consists of 14 nodes and 19 undirected edges, with an initial route count of 61 determined using the DFS algorithm. In contrast, the Germany-50 network comprises 50 nodes and 176 undirected edges, with an initial route count (K) capped at 10,000 and calculated using an enhanced DFS algorithm. Figure 3 shows the design and network topology of NSFNet-14 and Germany-50.  
Figure 3: The design and network topology of NSFNet-14 and Germany-50: (**a**) NSFNet-14, and (**b**) Germany-50.
# Tradeoffs in Key Rotation Strategies for Industrial Internet of Things Devices and Firmware
## 3 Discussion and Analysis of Tradeoffs
[PERSON] [22] recommends the concept of \"crypto agility\" in which one not only rotates keys but also rotates key algorithms. Architecture (E) allows the rotation of the algorithm and/or the key as long as the new certificates are cross-signed by the same chain of trust as the original. Therefore, although the Issuing CA cannot be changed on the device, the signing keys for any bootloader or firmware segments can be rotated, even by using new algorithms. Unfortunately, we are not aware of any secure way to rotate the immutable certificate of the Issuing CA in architecture (E). Architecture (D) provides the same capability but requires regular connectivity to IIOT services. For architecture (D) the IIOT services promote the separation of trust and cross-checking that yield additional flexibility in what can securely be performed on the device. The bottom line is that there are ultimate limits to what can be rotated. Making the issuing CA certificate on the device immutable will protect its authenticity and integrity and allow for rotation of firmware signing keys but only within the confines of issuing CA signing specifications (e.g., signing algorithm and existence of the CA). Figure 5: Tradeoffs between IIOT key rotation architectures (A) through (E) described in the previous subsections according to the measures defined in Table 1.  
Thinking about post-quantum brings up the concept of IIOT constraints, such as memory, processing capacity, and network bandwidth. Architecture (C) optimizes many of these features where there are heavy restrictions on resources; however, it limits the number of low-level rotations. Architecture (D) is designed around inexpensive chips that would be found in the automotive community with constrained memory and processor capabilities; while it requires connectivity, it has been designed for limited and potentially inconsistent connections. UPTANE does require additional public keys and metadata associated with how the device communicates with the director repository. Architecture (E) does not depend as heavily on connectivity but requires additional memory for storage of a certificate.