esa-sceva/satcom-chunk-collection huggingface dataset:
# Exploring Hybrid-Multimodal Routing to Improve User Experience in Urban Trips
## 5 Results and Discussion
### Active Transportation Acceptability  
The bikeability was obtained using Citi Bike NYC data, as discussed in Section 4.3. We collected data from trips during 2017 and created a cumulative normalized distribution function of the trip lengths (see Figure 8). The curve depicts the probability of some users to use a bicycle given the length of a trip. As its length rises, the number of users drops. This curvature is used as the depreciation factor, in terms of length of trips, when assessing the satisfaction of a given trip segment using bicycles. It is reasonable to use this data since we are using this service as a bicycle provider when evaluating multimodal routes using bike-sharing, i.e., we consider their bike dockers as pickup/drop-off locations when routing trips. Additionally, Figure 8 shows the walkability curve that was used in our work.  
To obtain the Original Data curve in Figure 8, trips starting and finishing at the same docker station are dropped, since the bicycles are not being used for mobility. To interpolate the curve and produce Equation (15), we used the highlighted data points (see the bikeability curve in Figure 8). The curve was interpolated as a five-degree polynomial function, whose outcome is a depreciation factor (interval \([0,1]\)) that multiplies the perceived time when using this travel mode. In the figure, the values start to drop fairly around 6 km; before, the impact of the bikeability in the trip's enjo  
Figure 8: Normalized cumulative distribution function for bike trips using the Citi Bike NYC service in 2017.  
walkability (i.e., Equation (14)) drops quicker than bikeability, as expected due to the use of bicycles to travel longer distances.
# Generating Job Recommendations Based on User Personality and Gallup Tests
## 3 Proposed Approach
### Similarity/Distance Metrics
#### 3.1.1 Weighted Manhattan Distance  
Manhattan distance, also known as the L1 distance, measures the absolute difference between corresponding elements of two vectors. In our problem, it is defined as:  
\[\text{Manhattan Distance}=\sum_{i=1}^{34}w_{i}\cdot|U_{i}-P_{i}| \tag{3}\]  
where \(\mathbf{U}\) is the user profile vector, and \(\mathbf{P}\) is the respective position profile vector. Both vectors are of size 34, and \(U_{i}\) and \(P_{i}\) represent the \(i\)-th components of the vectors \(\mathbf{U}\) and \(\mathbf{P}\), respectively. \(w_{i}\) is the weight assigned to the \(i\)-th trait. The weights are assigned to each trait depending on how high it ranks for the user \(U\), using a sigmoid function, as depicted in Figure 4 and Equation (4). The differences in highly ranked traits for the user are weighted more than those in lower ranked personality traits.  
\[w_{i}=\frac{1}{1+e^{-\frac{(x_{i}-17)}{n}}} \tag{4}\]  
A smaller Manhattan distance indicates higher similarity between the vectors.
## 3 Results
### Applications of the Phenotypic Query Approach
Additionally, to handle multiple occurrences of matching observations,experts may add aggregation functions (minimum, average, first, etc.). Lastly, phenotype classes can be annotated as inclusion or exclusion criteria, which will affect how PhenoMan processes the FHIR query results for these classes. By using the phenotype classes in a PheSO and the properties described above, PhenoMan can generate FHIR Search queries that are subsequently sent to an FHIR server via GET requests. The query results are then used to create reports on patients with matching conditions. The process can be broken down into the following steps. First, PhenoMan prepares an initial patient set, which is then further refined. This patient set is derived from master data constraints such as FHIR administrative gender [46], date of birth, or age ranges. Age ranges are converted into dates. An initial FHIR Search query may look like \"Patient?birthdate=gt1954-04-24&birthdate=le1980-04-24&gender=male\", and the result is a set of FHIR Patient resources. After the initial set of patients is obtained, PhenoMan generates corresponding queries for inclusion and exclusion criteria (see the examples in Table 1). The queries are sent to the FHIR server, and in the case of inclusion criteria, the results are combined with the initial patient set (set-theoretical intersection), whereas exclusion criteria results are removed from the initial patient set (set-theoretical difference). A more detailed description of FHIR query building and the result set handling process is presented in [23; 44].
## 3 Methodology
### Data Acquisition
#### 3.1.1 Data of Descriptive Analysis  
To perform the descriptive analysis, we collected air pollution levels from 2022 to 2023 in the three cities using the GEE and Sentinel-5 satellites. The descriptive statistics show the varying readings of air pollutants across the study cities. Readings were collected daily for two years in the target cities, with each day's reading representing the average readings of pollutant levels across the city area for a single day. In choosing the two years for this study, we aimed to avoid the years of the COVID-19 pandemic. Many researchers have observed a decrease in air pollution during those years, and we sought to investigate the effects of increasing urbanization in Riyadh.
# Seismic Performance Evaluation of Steel Buildings with Oil Dampers Using Capacity Spectrum Method
## 4 Application of the Proposed CSM on Steel Frame Buildings  
### Effective Damping Ratio of MDOF System  
Where the oil dampers were arranged in a diagonal scheme with the inclination angle, \(\theta\), for an MDOF system as illustrated in Figure 5, the Equations (4) and (5) can be used to incorporate the oil damper distribution along the building height, as follows:  
\[\sum\Delta W_{j}=\pi\cdot\omega_{eq}\sum C_{j}\;\phi_{rj}^{2}\cos\theta_{j}^{2} \tag{14}\]  
\[\mathrm{W}=\frac{1}{2}\omega_{eq}^{2}\;\sum m_{j}\;\Delta_{j}^{2} \tag{15}\]  
where \(C_{j}\), \(\Delta_{j}\), \(\phi_{rj}\), \(\omega_{eq}\) and \(m_{j}\) are the damping coefficient of the oil damper, the story drift of the first mode, the relative story drift, the equivalent circular frequency and the mass of \(j\)th floor, respectively. By substituting Equations (14) and (15) into Equation (4), the viscous damping ratio of the oil damper can be obtained as given in Equation (16) for an MDOF system.  
\[h_{V}=\frac{1}{2}\frac{\sum C_{j}\;\phi_{rj}^{2}\;\cos\theta_{j}^{2}}{\omega_{ eq}\sum m_{j}\;\Delta_{j}^{2}} \tag{16}\]
## 4 Results and Discussion
### The Optimal Design
In the off-design analysis, eight kinds of outlet pressure boundary conditions were set up. The mass flow rate-total pressure ratio curve and mass flow rate-total static efficiency curve were obtained, as shown in Figures 20 and 21. It can be seen from the figure that as the mass flow rate increased, the total pressure ratio and total static efficiency showed a decreasing trend. It is worth noting that the compressor performance was higher under the small flow condition deviating from the design condition. When the mass flow rate was 5.97 kg/s, the compressor had the highest pressure ratio and total static efficiency, 1.90% and 80% respectively. When the mass flow rate was in the range of 5.97-9.52 kg/s, the compressor performance curve changed gently. At this time, the compressor had a higher pressure ratio and efficiency. Under the condition of a mass flow rate less than 5.97 kg/s, the pressure ratio was too large, which led to the increase of outlet pressure and further caused the backflow and compressor surge. The sensitivity of mass flow rate to compressor performance was greatly increased while the mass flow rate was greater than 9.52 kg/s. The total pressure ratio and the total static efficiency decreased sharply with the increase of mass flow rate, and the blocking phenomenon was becoming more and more serious. As the mass flow rate continued to increase to 11.05 kg/s, the flow at a throat in the compressor channel reached a critical state. At this time, the flow rate was at its maximum. No matter how much the compressor  
Figure 19: The entropy distribution of different sections: (**a**) different blade height sections; (**b**) impeller outlet and (**c**) diffuser outlet. back pressure was lowered, the flow rate would no longer increase. The compressor would enter the blocking condition. The performance of the compressor optimal design in this paper was compared with the most advanced SNL centrifugal compressor in the current public literature, as shown in Figure 14:0 Compared with the compressor of SNL, the compressor speed of the optimal design was 40,000 rpm, and the speed was almost reduced to 50%. It is worth noting that in this paper, the design speed was greatly reduced. This means that it had more testability, lower motor cost, simpler system composition and other advantages. Meanwhile, the total pressure ratio was 1.9, bigger than 1.8 of SNL. Besides, in terms of total static efficiency, it was greatly exceeded, which was 13.16% higher than the compressor of SNL. In general, the compressor designed in this study had a low speed requirement and strong comprehensive performance. Figure 14:1 The compressor mass flow rate-total static efficiency curve. Figure 14:2 The compressor mass flow rate-total pressure ratio curve.
## 3 Results  
### Range of Motion (ROM) Results  
The results of the one-way repeated measure ANOVA showed that the majority of ranges of motion (ROM) in all segments increased with increasing intensity. (Table 2) There were significant differences in foot ROM in the sagittal plane (\(p\) = 0.04), and shank ROM in the transverse plane (\(p\) = 0.002) between different pedaling intensities. The results of the Bonferroni post-hoc showed that there were significant differences in shank ROM in the transverse plane between 50%-125% (\(p\) = 0.016) and 75%-125% (\(p\) = 0.042) intensities, and there were significant differences in foot ROM in the sagittal plane between 50-100% intensities (\(p\) = 0.041).  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Coordination** & **Abbreviation** \\ \hline
1 & \
\begin{tabular}{c} Thigh flexion/extension to shank flexion/extension \\ \end{tabular} &
\begin{tabular}{c} Thigh FL/EX—Shank FL/EX \\ \end{tabular} \\
2 & \
\begin{tabular}{c} Thigh flexion/extension to foot plantar flexion/dorsiflexion \\ \end{tabular} &
\begin{tabular}{c} Thigh FL/EX—Foot PF/DF \\ \end{tabular} \\
3 & \
\begin{tabular}{c} Thigh abduction/adduction to shank abduction/adduction \\ \end{tabular} &
\begin{tabular}{c} Thigh ABD/ADD—Shank ABD/ADD \\ \end{tabular} \\
4 & \
\begin{tabular}{c} Shank flexion/extension to foot plantar flexion/dorsiflexion \\ \end{tabular} &
\begin{tabular}{c} Shank FL/EX—Foot PF/DF \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table}
Table 1: The calculated segment coordination and its abbreviation.  
\begin{table}
\begin{tabular}{l c c c c c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Segments**} & \multicolumn{4}{c}{**Transverse Plane**} & \multicolumn{4}{c}{**Frontal Plane**} & \multicolumn{4}{c}{**Spatial Plane**} \\ \cline{2-13}  & **80\%** & **79\%** & **180\%** & **125\%** & **\(P\) Value** & **80\%** & **75\%** & **180\%** & **125\%** & **\(P\) Value** & **80\%** & **75\%** & **100\%** & **125\%** & **\(P\) Value** \\ \hline Thigh & 21.58 \(\pm\) 2.42 & 50.11 \(\pm\) 2.33 & 27.27 \(\pm\) 1.66 & 26.56 \(\pm\) 1.10 & 0.62 & 23.16 \(\pm\) 1.65 & 23.78 \(\pm\) 1.12 & 25.80 \(\pm\) 1.252 & 27.68 \(\pm\) 1.46 & 0.80 & 39.49 \(\pm\) 7.94 & 27.27 \(\pm\) 2.79 & 40.40 \(\pm\) 3.57 & 40.42 \(\pm\) 8.90 & 0.72 \\ Shank & 50.66 \(\pm\) 3.27 & 57.66 \(\pm\) 3.37 & 10.75 \(\pm\) 4.21 & 11.38 \(\pm\) 4.50 & 0.002\({}^{*}\) & 57.35 \(\pm\) 5.50 & 57.81 \(\pm\) 4.94 & 92.30 \(\pm\) 4.04 & 100.54 & 60.6 & 43.38 \(\pm\) 2.75 & 42.69 \(\pm\) 2.28 & 42.08 \(\pm\) 4.44 & 42.01 \(\pm\) 2.69 & 0.08 \\ Post & 90.8 \(\pm\) 5.11 & 14.48 \(\pm\) 8.93 & 12.59 \(\pm\) 7.56 & 14.67 \(\pm\) 7.55 & 0.09 & 11.8 \(\pm\) 1.33 & 12.79 \(\pm\) 1.71 & 14.36 \(\pm\) 1.95 & 16.43 \(\pm\) 1.65 & 0.17 & 43.03 \(\pm\) 1.10 & 43.11 \(\pm\) 1.07 & 53.30 \(\pm\) 14.54 & 46.76 \(\pm\) 11.18 & 0.04\({}^{*}\) \\ \hline \hline \end{tabular}  
* Significant differences between intersstiles at \(\alpha=0.05\)  
\end{table}
Table 2: Segment ranges of motion (ROM) in transverse, frontal, and sagittal plane during pedaling at different intensities.
# Comprehensive Study of IoT Vulnerabilities and Countermeasures
## 1 Introduction
### Organization of the Paper  
The paper is structured to provide a comprehensive exploration of IoT security challenges and potential solutions. The first section, Section 2, Preliminary Research, introduces foundational IoT concepts, including an in-depth examination of the five-layer IoT architecture and its applications across various industries. This section highlights how IoT is integrated into domains such as smart cities, healthcare, transportation, industrial automation, agriculture, and military operations. By outlining these diverse applications, we establish the security challenges unique to each IoT deployment. Next, we have Commonly Used Protocols which provides an overview of the various communication protocols that enable IoT functionality. This includes both widely adopted and lesser-known protocols used in IoT ecosystems, each with its own security considerations. Understanding the strengths and weaknesses of these protocols is critical for evaluating their role in securing IoT networks. Following this, Section 3, IoT Vulnerability Layers, delves into the specific vulnerabilities present within IoT systems, categorizing them into hardware, software, network, and sensor network vulnerabilities. This section examines how these weaknesses impact the confidentiality, integrity, and availability of IoT devices and infrastructure. By understanding these vulnerabilities, we can better assess the risks that IoT environments face and identify potential attack vectors that adversaries may exploit. Then, Section 4, Significance of Our Paper, will go over other recent surveys that are similar to ours. The goal of this section is to differentiate what makes our paper unique compared to others. Finally, Section 5, Conclusions and Future Research to be Worked, summarizes the key findings of this study and discusses the importance of continuous research in IoT security. As technology advances, new vulnerabilities and attack techniques will emerge, requiring ongoing improvements to security frameworks. Future research directions will focus on refining existing countermeasures, addressing newly discovered threats, and ensuring that IoT systems remain secure in the face of evolving challenges. Overall, this paper seeks to contribute to the development of secure IoT environments by thoroughly analyzing vulnerabilities, attack methodologies, and effective mitigation strategies. By understanding the risks and implementing proactive security measures, we can enhance the resilience of IoT systems against both current and future threats.
# An Efficient Ultra-Tight GPS/RISS Integrated System for Challenging Navigation Environments
## 3 The Proposed Method
### GPS/SD RISS Integration  
Looking into Equations (1) to (10) one can see that the major source of error in the RISS system is the gyroscope measurement error (mainly gyroscope bias) since it is inherent in the azimuth calculations which is further enlarged when integrated to calculate the horizontal velocity and then horizontal position components [12]. The accelerometers errors have a small effect since no integration is involved in the roll and pitch calculations equations. Thus, the roll and pitch angles are not included in the integrated solution.  
Figure 5: The Proposed Ultra Tight Integrated System.
## 5 Modeling of the Proposed Protocols
### DDSec Protocol
In this case, BBU pool finds a match from \(\mathit{UE}_{A},gNB\) sets up a link with \(\mathit{UE}_{B}\) and forwards (\(\mathit{UB}\)), \(DataName\) and \(PK_{\mathit{U}E_{B}}\) to \(\mathit{UE}_{A}\) in \(\mathit{DiscMsg}\) message encrypted with key \(K_{gNBa}\). Msg4.gNB\(\rightarrow\)UEB:((LinkUpMsg),{KgNBb})  
\(gNB\) forwards \(\mathit{UE}_{A}\)'s (\(\mathit{UA}\)), data name \(DataName\), logical address \(nAdd\) and public key \(PK_{\mathit{U}E_{A}}\) to \(\mathit{UE}_{B}\) in \(LinkU\mathit{Mgg}\) message encrypted with key \(K_{gNBb}\). Msg5.UEA\(\rightarrow\)UEB:((PublMsg),{PKUEB})  
After the confirmation of link quality of the allocated channel, \(\mathit{UE}_{A}\) initiates the authentication request by sending a \(PublMsg\) message that includes its pseudonym ID (\(\mathit{UA}\)), data name \(DataName\), data ID (\(D1\)), a nonce \(Rand\_a\) as authentication challenge, a timestamp \(T_{S1}\) and hash of the message \(hash(UA,DataName,D1,Rand\_a,T_{S1})\) signed with its private key \(SK_{\mathit{U}E_{A}}\). and \(PublMsg\) is encrypted with \(\mathit{U}E_{B}\)'s public key \(PK_{\mathit{U}E_{B}}\). The hashing is used for integrity and message authentication and timestamp for replay attack protection. Msg6.UEB\(\rightarrow\)UEA:((LookUp),{PKUEA})  
When \(\mathit{UE}_{B}\) receives Message 5, it responds with a \(LookUp\) message that includes its pseudonym ID (\(\mathit{UB}\)) and data name pseudonym ID \(D1\), confirming the data name and its ID. \(\mathit{UE}_{B}\) also returns the nonce \(Rand\_a\), together with new nonce \(Rand\_b\), a timestamp \(T_{S2}\), hash of the whole message \((\mathit{UB},D1,Rand\_a,Rand\_b,T_{S2})\) signed with its private key \(SK_{\mathit{U}E_{B}}\) and encrypt the \(LookUp\) message with \(\mathit{UE}_{A}\)'s public key \(PK_{\mathit{U}E_{A}}\). \(\mathit{UE}_{B}\) authenticates \(\mathit{UE}_{A}\) by sending back \(Rand\_a\) together with \(Rand\_b\). Msg7.UEA\(\rightarrow\)UEB:((SendData),{PKUEB})  
When \(\mathit{UE}_{A}\) receives Message 6, it generates attribute and capabilities token \(Acap\) using the permission delegation authorization granted by original data owner. Then, \(\mathit{UE}_{A}\) returns \(Rand\_b\) to confirm a successful authentication of \(\mathit{UE}_{B}\) together with the requested \(DATA\), \(Kd1\) symmetric key to decrypt the data, \(ACap\) token authorizing \(\mathit{UE}_{B}\) to cache, share data and delegate its access rights to another UE and hash of the symmetric key and the token \(hash(Kd1,ACap)\) signed with \(SK_{\mathit{U}E_{A}}\) in \(SendData\) message encrypted with \(PK_{\mathit{U}E_{B}}\).
# A Comparative Study of the Application of Different Commercial Software for Topology Optimization
## 6 Comparison of the Used Commercial Software
### SolidWorks  
SolidWorks was chosen for the design of the models due to its user-friendly CAD interface. The software provides a plethora of exporting methods of the 3D models, such as the widely supported, standardized formats, IGES and STEP. The SolidWorks FEM module for the pre-processing task is quite capable and includes any possible analysis. However, ANSYS Mechanical was used for both the finite element analysis of the initial designs and  
Figure 11: The results of the validation studies for the bridge for 50% and maximum weight reduction in (**a**) SolidWorks, (**b**) ANSYS Mechanical, and (**c**) ABAQUS.  
Figure 10: The optimized designs of the bridge for 50% and maximum weight reduction in (**a**) SolidWorks, (**b**) ANSYS Mechanical, and (**c**) ABAQUS.  
the numerical validation studies of their optimized designs. It was crucial to use the same software for all FEA studies for comparison reasons. In addition, ANSYS has different mesh capabilities and can easily repair bad geometries in a faceted geometry that could possibly be created by TO.  
The TO in SolidWorks is quite easy, but its TO module offers limited capabilities and options. The supported objective functions are stiffness, mass, and displacement, while displacement, mass, frequency, stress, and FOS can be used as constraints. Furthermore, it offers member size, mold (pull direction), and planar symmetry in addition to the standard manufacturing constraints. The TO module creates stress, displacement, strain, and FOS plots for the optimized designs. However, the software platform claims that these are only rough estimates and recommends redesigning the model for a validation study. In addition, the TO module gives the possibility to change the isosurface values (isovalues) and, thus, remove/add more material from the optimized design by using elements with a relative density higher than a specific number (element density distribution plot). The isovalue is, by default, equal to 0.3. The optimized models in SolidWorks are faceted geometries that can be exported as STL files or transferred to a validation study.  
Concerning the post-processing of the optimized designs, SolidWorks focuses mostly on the design for CPM and not so much on the design for AM. It contains an automatic smoothing tool with 11 cycles from coarse to smooth quality. The smoothed geometry can be exported either as an STL file ready for 3D printing or as a graphics, solid, or surface file for further editing. The optimized design can easily be imported to the CAD module for redesigning. However, a complete 3D preparation module with manual smoothing and facets editing tools is missing.  
SolidWorks optimized the models in less time compared to ANSYS Mechanical and ABAQUS. In addition, it gave the heaviest designs of the bell crank lever and pillow bracket but created the lightest bridge with a big difference from the other two software platforms. It seems that the TO module of SolidWorks is more effective for large structures.
# Error-Resistant Movement Detection Algorithm for the Elderly with Smart Mirror
## 4 Movement Detection Algorithm for the Elderly in Smart Mirror  
### Overall Structure of Movement Detection System  
The motion detection method in this study uses the displacement of the joint coordinates. Calculating the displacement is not limited to two adjacent frames but uses more previous frames (frame depth, 'RecentFrame'). If the corresponding displacements are greater than 0 (which is the threshold value), it may be determined that the corresponding frame is in a moving state; otherwise, it may be determined that they are in a non-moving state. However, due to the existence of natural error, which will be described later, the threshold value cannot be set to 0. And due to the existence of detection error, which will be described later, movement detection should be determined not by single frame result but by the majority result of recent frames. In Figure 2, overall structure of movement detection system in the proposed study is described.  
The algorithm that used a fixed threshold value might have a potential weakness for movement with varying distances, like the movement towards the camera. So, as the  
Figure 2: Overall structure of movement detection system in the proposed study. Movenet is used as a skeletal pose-detection model. The Vector Sum Method (VSM), to be discussed in Figure 6, is used as a movement detection algorithm. N represents the parameter RecentFrame in this flowchart. M represents parameters for ignoring single-joint movement, and N/2 represents the majority number of recent frames for detection error.  
threshold value must vary with distance, the shoulder width of the current target is set as a threshold value. Different threshold standards may be selected according to the purpose of the system. In this system, detecting the presence of minor movement in a smart mirror is the purpose.  
The pose estimation model has some limitations. The limitations of the pose estimation model hinder movement detection. The limitations will be discussed in the next session. The system in this study aims to overcome the pose estimation model's limitations for movement detection.