esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Application of 3D-printed Models in Pre-Operative Planning of Primary Total Knee Arthroplasty  
A careful pre-operative planning is mandatory in primary TKA to obtain a well-positioned, stable implant and, consequently, good outcomes [37; 38; 39].  
Pre-operatively, surgeons should focus on comorbidities, patient's history and characteristics (e.g., previous surgery, level of activities, etc.) and a complete knee evaluation. In particular, clinical evaluation should include Range of Motion (ROM), skin (previous scars) evaluation, patients' gait, neurovascular status, patellar tracking and stability evaluation, as well as the complete lower limb alignment. Furthermore, presence of foot abnormalities, especially in valgus knee, should be evaluated and, in some cases, addressed pre-operatively [37; 38; 39]. Evaluating all these characteristics is useful to choose the correct implant design and the level of constraint in primary TKA.  
For a correct pre-operative planning, including implant's size and positioning, a complete imaging is also mandatory. An X-ray panel should include a knee antero-posterior weight-bearing view (AP), a weight-bearing lateral view (LL), a full-length hip-to-ankle AP weight-bearing view and a patellar view [37]. Radiographic planning should include evaluation of the Hip-Knee Angle (HKA), with consequent description of the type (varus or valgus) and amount of the deformity. The second step is to define the femoral Valgus Cut Angle (VCA), which may be standardized (e.g., \(3^{\circ}\), \(5^{\circ}\) or \(6^{\circ}\)), or based on HKA [41].  
A femoral cut is then simulated based on VCA, and a tibial cut is also simulated based on the chosen alignment (mechanical or Kinematic). At the time of pre-operative planning, osteophytes are also recognized on AP and LL view, and the patella is evaluated. Lastly, implant size and positioning can be simulated according to dedicated templates. Recently, different software for digital templating has been proposed, with improvement in pre-operative planning accuracy [42]. Table 1 summarizes the current pre-operative planning.  
However, some primary TKA can be defined as \"complex\", due to severe ligament deficiency, post-traumatic bone losses, extra-articular deformities and comorbidities (e.g., poliomyelitis) [37]. In these cases, an accurate pre-operative planning can be challenging, so different authors proposed using a Patient Specific Instrumentation (PSI) to improve implant positioning and outcomes [43,44]. However, PSI should be reserved to complex primary TKA to improve its cost-effectiveness [45].  
There may be another option to simplify pre-operative planning and visualization in primary TKA: a 3D-printed knee model for pre-operative planning, as demonstrated by the following case.
## 4 Literature Review and Data Extraction
### Resource-Aware Task Allocation
It has been established that DS-UPP complies with the \"differential privacy with the restrictions that privacy budget is\" a recovery error. To solve this problem, the authors provided the mathematical lower bound and upper bound of the number of participants needed for the task achievement as well as the typical quantity of data that should be supplied by a participant. Lastly, they applied the algorithm PrivKV for comparison. According to their experimental findings, DS-UPP gained lower costs for sensing by roughly 90% while maintaining privacy and data quality standards. Moreover, [PERSON] et al. [62] addressed the drawbacks of the aforementioned current technologies. The paper created a platform for intelligent operation inspection that relies on a multi-agent system and is used for the live line measurement of substation equipment. The multi-agent system technology was used in the creation of the operational framework. The paper proposed integrating edge computing into the data processing stage of a multi-agent system, which enables the intelligent operation inspection platform to overcome problems associated with high latency, network instability, and limited resources. This integration also leads to increased flexibility, reliability, and resource utilization. This design enhances the systematization and intelligence level of substation operation inspection by creating an intelligent operation inspection system for substation live inspection. This covers various aspects such as decision making, wireless communication, data processing, data sensing, and analysis. Lastly, [PERSON] and [PERSON] [70] discussed the deployment of UAVs in 5G and beyond MEC scenarios, along with how they may be used as mobile base stations and communication relays. This paper described the difficulties in implementing flying edge computing. The authors suggested a multi-tier architecture using UAVs with various mobility models, and assesses the effectiveness of UAVs as edge nodes. This study also described the benefits of a multi-tier viewpoint and talked about the topology and design of a smart city network. The article concludes by summarizing related research on UAV networks, job offloading, and edge computing models that might increase the available computational resources, and it highlights current research gaps and future directions.
## 2 Materials and Methods
### Optimization  
Within the scope of the design optimization study, FEA of 27 cases was performed to examine the effects of three design variables. The obtained FEA results were processed and optimized via the MATLAB R2022b program. The flowchart of optimization is shown in Figure 9 and the optimization steps below were carried out.  
First, the design variables (p1, p2 and p3) and the lower and upper values for each parameter were defined. The sample space was created via a factorial design. Twenty-seven different configurations were determined and obtained with the parametric design. Pick force and energy absorption values were obtained for each design.  
Next, a modeling approach using artificial neural networks (ANNs) was selected. ANNs were trained to learn the relationships between the design variables and the FEA results. In this way, robust models were created for the influence of design variables and the prediction of FEA results. The input data were used to predict the performance of the battery box side profiles. The ANN was trained with training data and then verified with test data (70% of the data were used for training, 15% for validation and 15% for testing, which were performed randomly).  
The trained ANN model was subsequently combined with a multi-objective optimization method. In this study, a multi-objective optimization technique was used to find the  
Figure 9: Flowchart of optimization.  
optimal combination of design variables. Multi-objective optimization aims to optimize multiple objective functions simultaneously. The optimal result contains the design variable values that best satisfy the specified objective functions. The objective function of the optimization is to maximize the mass gain and maintain the strength at a sufficient and safe level without over-engineering. Optimization was performed via the nondominated sorting genetic algorithm-II (NSGA-II), a popular multi-objective optimization algorithm known for its fast and specific approach [49; 50]. NSGA-II can find a set of Pareto-optimal solutions that represent the best trade-offs between conflicting objectives.  
Finally, determining the best outcome on the Pareto front curve resulting from multi-objective optimization requires a preferred decision mechanism or method. This decision mechanism is often referred to as \"solution selection\" and is determined depending on the requirements, objectives and preferences of the design problem. For this reason, the TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution) method, which is frequently used in solving multifaceted decision making problems, is used in this study [51]. This method evaluates alternative solutions on the basis of different criteria and helps to determine the best solution.
## 3 Results and Discussion
### Operation
The cause of this error is unknown but is likely due to an intermittent I\({}^{2}\)C sensor bus communication failure. The most significant issue was in v2.0, when a loss of communication with all eight beacons was experienced in November 2019. The cause of this issue was initially suspected to be related to the onset of winter and the inability of the RockBLOCK transceiver's integrated patch antenna to transmit through a snow cover. However, once the beacons began transmitting again in April 2020, it was determined that the issue was related to temperature and power. In v2.0, the RockBLOCK was powered by a single 3.3 V 600 mA step-down voltage regulator, which, at temperatures below \(-20\)\({}^{\circ}\)C, could no longer provide enough power to successfully power up the RockBLOCK to transmit data. Despite 3.3 V being listed as a supported voltage, after consulting the manufacturer, Ground Control, it was determined that the RockBLOCK requires a minimum of 5 V to be supplied when operating in environments with low temperatures. This behaviour was consistent over time, with loss of communications occurring again in the fall of 2021 and 2022. To address this issue, all future Cryologger versions include a 5 V voltage regulator. An additional issue encountered with a limited number of v2.0 beacons was a tendency to become inverted after deployment, which led to the eventual ingress of water in shallow meltwater ponds and subsequent failure of the electronics. Measurements from the accelerometer were used to confirm when the beacons had flipped upside down. In this orientation, it was observed that the recorded GNSS positions were less accurate, and Iridium transmissions also took longer and failed more frequently. A decrease in temperature inside the enclosure indicated that water ingress occurred before the loss of communication. This was likely a result of the enclosure used in v2.0, which was smaller and lighter and subsequently more susceptible to being blown over by strong wind gusts than the larger and heavier enclosure used in v1.0. Having learned from this, we now recommend attaching the case to a wooden cross or similar structure. Figure 8: Diagnostic variables recorded from ITB 300434063415110 between 27 August 2018 and 3 July 2019 showing: (**a**) daily mean internal temperature; (**b**) daily maximum pitch (solid orange) and roll (dashed green) angles; (**c**) daily mean number of GPS satellites in view; (**d**) daily mean transmission durations; (**e**) daily minimum battery voltages.
# A Novel Protocol Using Captive Portals for FIDO2 Network Authentication +
## 2 Related Work
### Network Authentication with FIDO  
In 2018, [PERSON] [13] presented a solution that makes use of the FIDO UAF protocol, a previous version of the current FIDO CTAP protocol, to network authentication. In this work, the protocol is used for authenticating guest users in an enterprise network by employing their personal Android smartphones. For this purpose, they created a scheme where guests can register themselves to be authenticated in a different Wi-Fi network.  
FIDO protocols were also applied to IoT network authentication. Also in 2018, [PERSON] proposed a security authorisation scheme for IoT devices, which interacts with the user Android smartphone to perform FIDO CTAP1 authentication [14]. Then, in 2021, [PERSON] et al. [15] used FIDO CTAP1 security keys to provide user authentication in an IoT Smart Home environment. In their work, a gateway node managing IoT devices authenticates a user when performing management operations. This authentication is performed with FIDO CTAP1 security keys physically connected to the gateway node.  
Furthermore, it is worth mentioning the work of [PERSON] [16], who proposed in 2022 a solution for connecting to a VPN service by authenticating the user with FIDO security keys. Their approach is to create an intermediary web portal that provides a temporal username and password pair after authenticating with security keys. Their approach is a wrapper solution to the problem, as the authentication of the VPN service is still based on the existing compatible authentication method of the VPN software.  
On the other hand, it is interesting how captive portals have been adapted to additional network authentication scenarios. [PERSON] et al. [17] designed a custom EAP method named EAP for Secure Hotspots (EAP-SH) that adapts the web-based authentication of captive portals to be used in IEEE 802.1X access controls. The work of [PERSON] et al. [17] can be adapted to use the work of this paper with EAP technology.
## 3 Proposed Method
### Facial Emotion Feature Extraction Based on Geodesic Distances and Sample Entropy
#### 3.3.1 Spatial and Temporal Features of Cognitive Emotions Based on Geodesic Distances  
The spatial features of emotional description points are often expressed as distances. The traditional Euclidean distance reflects the linear relationship between feature points, but cannot represent the intrinsic manifold structure between them. Due to factors such as lighting, posture, and facial expressions, high-dimensional facial image data exhibit significant nonlinear manifolds [61]. In differential geometry, a geodesic is a curve representing, in some sense, the shortest path between two points on a surface. Compared with the common Euclidean distance, geodesic distance can describe the actual distance of a point on a sphere more accurately. For a human face in an image, which is composed of a large number of pixels and arcs, geodesic distances can more accurately express the  
Figure 4: Process of positioning emotion description points based on AAM.  
actual spatial characteristics of facial emotions. Note that the geodesic distance between any two points \(s_{i}\) and \(s_{j}\) is \(d_{g}(s_{i},s_{j})\).  
According to the conclusion of psychological experiments, the average duration of cognitive emotions is 1-4 s. Therefore, we should also consider the temporal characteristics of cognitive emotions.  
If each video sample contains \(n\) images, the set of emotion description points for each sample can be expressed as follows:  
\[Sample=\{S_{1},S_{2},\ldots,S_{n}\} \tag{8}\]  
We calculated the geodesic distances between the emotional description points in the first image and the corresponding emotional description points in other images in each sample. The space and temporal feature matrix of emotion description points was as follows:  
\[\begin{split}\boldsymbol{Feature_{\text{st}}}&= \left|\begin{array}{cccc}d_{\text{g}}(s_{1,1},s_{1,1})&d_{\text{g}}(s_{1,1},s_ {2,1})&\ldots&d_{\text{g}}(s_{1,1},s_{n,1})\\ d_{\text{g}}(s_{1,2},s_{1,2})&d_{\text{g}}(s_{1,2},s_{2,2})&\ldots&d_{\text{g}}( s_{1,2},s_{n,2})\\ \ldots&\ldots&\ldots&\ldots\\ d_{\text{g}}(s_{1,49},s_{1,49})&d_{\text{g}}(s_{1,49},s_{2,49})&\ldots&d_{ \text{g}}(s_{1,49},s_{n,49})\\ \end{array}\right|\\ &=\left|d_{g}(V_{1,1},V_{i,1})&d_{\text{g}}(V_{1,2},V_{i,2})&\ldots&d_{g}(V_{ 1,49},V_{n,49})\right|^{T},1\leq i\leq n\end{split} \tag{9}\]  
where \(d_{\text{g}}(V_{1,j},V_{i,j})=\big{\{}d_{\text{g}}(s_{1,i},s_{1,j})\)\(d_{\text{g}}(s_{1,j},s_{2,j})\)\(\ldots\)\(d_{\text{g}}(s_{1,j},s_{n,j})\big{\}},1\leq j\leq 49\).
## 4 Matching Calculation Based on the Sensitivity Method
In addition, it should be noted that considering the high signal-to-noise ratio of the corresponding response value at the resonance point, the weight at the resonance point should be set to a large value, while that at the non-resonance point should be set to a small one.
## 1 Introduction  
In recent years, an undoubtedly profound improvement has been observed in the diagnostics of patients who have suffered from traumatic injuries due to the accessibility of crucial diagnostic tools such as a computer tomography (CT) scan, for instance [1, 2]. Nonetheless, traumatic pelvic fractures remain one of the most life-threatening injuries given their characteristics and the concomitant injuries that frequently occur [3, 4]. Despitethe fact that pelvic fractures account for only about 3-8% of all skeletal fractures, mortality rates in specific studies were shown to be as high as 15% ([PERSON] et al., 2016; [PERSON] et al., 2017). Many recent publications that concern pelvic fractures are, in fact, focused on blood loss, survival, and initial management ([PERSON] et al., 2016; [PERSON] et al., 2017; [PERSON] et al., 2018; [PERSON] et al., 2019; [PERSON] et al., 2019; [PERSON] et al.  
sacroiliac complex. APC III injuries are associated with complete posterior ligamentous disruption and have the highest rate of mortality, blood loss, and need for transfusion of all pelvic ring injuries.  
Lateral compression (LC) injuries result when a laterally based force directed medially is applied to the pelvis. LC I injuries result from a lateral force delivered over the posterior aspect of the pelvis and represent a spectrum of injuries [19]. LC II injuries result from a more anteriorly directed force, causing internal rotation of the anterior hemipelvis with possible external rotation of the posterior hemipelvis, with the anterior sacroiliac joint serving as a fulcrum. The resulting posterior pelvic injury in LC II patterns may be a sacral fracture, sacroiliac ligament and joint disruption, or crescent fracture-dislocation of the ilium. LC III injuries result from greater force. The rotation of the ipsilateral hemipelvis causes the injury to the contralateral hemipelvis, resulting in LC III.  
Vertical shear (VS) injuries result from an axially loaded force delivered over one or both hemipelves lateral to the midline. The sacrum is driven down relative to the iliac wing, resulting in complete ligamentous injury and disruption of the sacrospinous, sacrotuberous, anterior, and posterior sacroiliac ligaments on the injured side [19].  
Complex injury patterns are a combination of any three primary patterns (APC, LC, or VS), usually resulting from LC injuries mixed with AP or VS trauma.
## 2 Design of Underwater Stacked Capacitive Power Transfer Coupler
### Configuration of Underwater Stacked CPT System  
Figure 4 shows the design of the underwater stacked CPT coupler proposed in this study. As shown in Figure 4a, the square front plate and back plate are separated by a gap of 2 mm to implement a stacked structure, and the lengths of the plates are 80 mm and 100 mm, respectively. In order to prevent direct contact between water and the copper plate, the stacked structure is placed inside an acrylic box, and the width, length, and height of the acrylic box are 105 mm, 110 mm, and 7.5 mm, respectively. The detailed parameters of the proposed underwater stacked CPT coupler are shown in Table 2. As shown in Figure 4b, the transmitter and receiver with the same configuration are positioned facing each other underwater. In this study, the distance \(d\) between the transmitter and receiver is considered from 10 mm to 200 mm.  
In Figure 5a, we can see the stacked CPT coupler fabricated based on the design of Figure 4. The dielectric constant of the FR-4 substrate \(\varepsilon\) is 4.4 and has a thickness of about 1.52 mm. The SMA connector is connected to the end of the matching circuit, and it is connected to a vector network analyzer (VNA) to measure the transmission characteristics of the transmitter and receiver in the experiment. Figure 5b shows the experimental setup.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Notation** & **Value** & **Notation** & **Value** \\ \hline \(a\) & 80 mm & \(c_{1}\) & 105 mm \\ \(b\) & 100 mm & \(c_{2}\) & 110 mm \\ \(m_{1}\) & 18 mm & \(c_{3}\) & 7.5 mm \\ \(m_{2}\) & 30 mm & \(Gap\) & 2 mm \\ \hline \hline \end{tabular}
\end{table}
Table 2: Specifications of underwater stacked CPT coupler.  
Figure 4: Underwater stacked CPT coupler design. (**a**) Single coupler quarter view. (**b**) Front view.
# Human Activity Signatures Captured Under Different Directions Using SISO and MIMO Radar Systems
## 4 Human Activity Signatures by Using a Distributed MIMO FMCW Radar System
### Results for the Proposed \(2\times 2\) MIMO Configuration
For Scenario 2, as the direction of the activity is towards Radar\({}_{2}\), we observe that  
Figure 8: For Radar\({}_{2}\), the measured radial velocity distribution \(p_{22}(v,t)\) and mean radial velocity \(\sigma_{22}(t)\) of a human falling activity in **(a)** Scenario 1, **(b)** Scenario 2, and **(c)** Scenario 3. Figure 9: For Radar\({}_{2}\), the measured radial velocity distribution \(p_{22}(v,t)\) and mean radial velocity \(\widehat{v}_{22}(t)\) of a human walking activity in **(a)** Scenario 1, **(b)** Scenario 2, and **(c)** Scenario 3. Figures 8b and 9b give better activity signatures than Figures 2b and 3b, respectively. In other words, Radar\({}_{2}\) produces better human activity signatures than Radar\({}_{1}\) for Scenario 2. In Scenario 3, the direction of movement is roughly diagonal to the boresights of Radar\({}_{1}\) and Radar\({}_{2}\), as shown in Figure 7. For the human falling activity, the initial and final positions are \((x_{3},y_{3})\) and \((x_{1},y_{1})\), respectively. This is in contrast to the walking activity, where the initial and final positions are \((x_{1},y_{1})\) and \((x_{3},y_{3})\), respectively. Figures 2c and 8c show the TV radial velocity distribution \(p_{ii}(v,t)\) of the falling activity with respect to Radar\({}_{1}\) and Radar\({}_{2}\). Analogously, Figures 3c and 9c show the TV radial velocity distribution \(p_{ii}(v,t)\) of the walking activity corresponding to Radar\({}_{1}\) and Radar\({}_{2}\), respectively. Note that both Radar\({}_{1}\) and Radar\({}_{2}\) capture adequate human activity signatures for Scenario 3. It is evident from the measurement results that unlike the SISO radar system, the distributed MIMO radar system has the innate capability to provide better multi-perspective human activity signatures for all three scenarios.