esa-sceva/satcom-chunk-collection huggingface dataset:
# Effect of Seat Condition on Abdominal Injuries to Vehicle Occupants in Frontal Impact Accidents
## 1 Introduction
Figure 2 shows a photograph of residual deformation of the front passenger car seat after a Japan new car assessment program (JNCAP) full frontal rigid barrier impact test, in which a Hybrid III dummy was seated in the passenger seat during the test. The frontal edge of the seat exhibits residual \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \multicolumn{3}{c}{**Number**} & \multicolumn{3}{c}{**Rate (\%)**} \\ \cline{2-6}
**Main Body Region** & **(a) Minor Injured** & **(b) Serious Injured** & **(c) Fatality** & **Serious Injury** & **Fatality Rate** \\  & **Occupants** & **Occupants** & **Occupants** & **Rate b/(a+b)** & **c/(a+b+c)** \\ \hline Head & 9047 & 738 & 305 & 8 & 3 \\ Face & 3507 & 304 & 14 & 8 & 0 \\ Neck & 302,051 & 2325 & 67 & 1 & 0 \\ Chest & 12,348 & 2836 & 444 & 19 & 3 \\ Abdomen & 1558 & 511 & 147 & 25 & 7 \\ Back & 3009 & 141 & 3 & 4 & 0 \\ Hip & 22,329 & 937 & 36 & 4 & 0 \\ Arms & 8334 & 985 & 5 & 11 & 0 \\ Legs & 7259 & 1251 & 28 & 15 & 0 \\ Total & 369,442 & 10,028 & 1049 & - & - \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Severe injury and fatality rates for individual body regions of vehicle occupants involved in traffic accidents in Japan in 2017.  
deformation of 100 mm, which suggests the seat moved downward along with a small forward rotation. Therefore, in this study, we select two conditions: 150 mm vertical downward movement of the seat [8] and 150 mm vertical downward movement in combination with forward rotation.
# Application of Spinel and Hexagonal Ferrites in Heterogeneous Photocatalysis
## 4 TiO\({}_{2}\)-Based Magnetic Photocatalysts
### Hexaferrites with TiO\({}_{2}\)  
Although pure hexaferrites have gained moderate attention as photocatalysts, their combination with other photoactive materials have frequently been reported. Among them, titania-based photocatalysts are the most common (see Table 4). For example, BaFe\({}_{12}\)O\({}_{19}\)/TiO\({}_{2}\) composites, initially reported by [PERSON] et al. [105] and [PERSON] et al. [106], can be prepared by a simple TiO\({}_{2}\) coating on the surface of the ferrite. In both studies, procion red MX 5B was used as a target pollutant decomposed under UV irradiation (no reusability analysis). It was found that though the activity increased with an increase in TiO\({}_{2}\) content, it was always lower than that of the P25 reference and/or sole TiO\({}_{2}\) NPs. In contrast, [PERSON] et al. found higher activity for the SrFe\({}_{12}\)O\({}_{19}\)/TiO\({}_{2}\) composite (0.2/0.8 for Sr-ferrite and TiO\({}_{2}\), respectively) than that for single components under UV irradiation [107]. Moreover, reusability was also confirmed in three consecutive cycles, with an activity decrease of ca.  
Figure 5: Possible reactions in Fe\({}_{3}\)O\({}_{4}\)/TiO\({}_{2}\) composite (drawn based on [101]).  
Figure 6: STEM images of Fe\({}_{3}\)O\({}_{4}\)@SiO\({}_{2}\) (**a**) and Fe\({}_{3}\)O\({}_{4}\)@SiO\({}_{2}\)/TiO\({}_{2}\) (**b**) combined with Ti, Fe, and Si mapping. Reprinted with permission from [102]. Copyright (2017) Creative Commons Attribution.  
10%. Furthermore, in contrast to pristine TiO\({}_{2}\) NPs, the composites also exhibited activity under visible-light (\(\lambda\geq\) 420 nm) irradiation. In both cases (UV and visible-light irradiation), 100% degradation was observed after approx. 4-5 h of irradiation. It has been proposed that enhanced activity is caused by the possible formation of a n-n type heterojunction between both phases. Another SrFe\({}_{12}\)O\({}_{19}\)-based composite was also prepared by [PERSON] et al. [108]. This time, three-phase composites, i.e., core/interlayer/shell structure, were prepared using SrFe\({}_{12}\)O\({}_{19}\), SiO\({}_{2}\), and TiO\({}_{2}\) as a core, an interlayer, and an outerlayer, respectively. The efficiency of the composite was confirmed during degradation of MB, reaching ca. 80% degradation during 3 h of UV irradiation, with high reusability since only an 8% activity decrease was observed after three cycles. As a reference, SrFe\({}_{12}\)O\({}_{19}\)/SiO\({}_{2}\) composite was used, showing negligible photocatalytic activity. Finally, PbFe\({}_{12}\)O\({}_{19}\)/TiO\({}_{2}\) composite was also investigated by [PERSON] et al. for degradation of Acid Blue 29, Acid Violet 7, and Acid Black 1 dyes [109]. The effective decolorization (based on the presented photographs) was observed for all tested dyes during 20\(-\)40 min of UV irradiation, but detailed process kinetics, as well as reusability, were not examined.
## 7 Conclusions  
In this paper, we investigate a hybrid analog/digital scheme with CPSs and antenna selection technique based on matching theory for uplink cell-free mm-Wave massive MIMO systems to achieve two objectives: enhancement of the EE and low computational complexity. These two objectives have been addressed by introducing a novel matching scheme based on the Hungarian algorithm for maximum weight matching. In a first step, the assignment optimization problem is formulated to match the RF chains at each AP in the coverage area and several sets of activated antennas based on the channel magnitude. In a second step, the Hungarian method is proposed to solve the formulated optimization problem due to its computational complexity. The efficiency of the proposed matching scheme for the antenna selection technique is justified by the simulation results, with several scenarios of uplink cell-free mm-Wave massive MIMO systems, which show that the matching approach can attain around 20% EE improvement and 200% complexity reduction compared to the state-of-the-art schemes. Thanks to the matching theory, half of the RF chains at each AP are connected to a minimum number of selected antennas in the proposed matching method. In contrast, each of the remaining RF chains is connected to a large number of selected antennas. Based on this finding, the tradeoff between SE, total power consumption, and computational complexity can be guaranteed. For future work, the proposed antenna selection based on the matching technique can be integrated with low-resolution ADCs due to the capability of our proposed scheme based on channel quality to enhance EE without significant loss in SE with low computational complexity.  
Figure 10: EE and SE and the complexity tradeoff as a function of the number \(M\) of APs when \(M=\{16,32,48,64,80\}\), \(K=8\), \(\rho=23\) dBm, \(N_{Q}=8\) CPSs, and \(N_{r}=\{48,80\}\) for the proposed matching scheme in uplink cell-free mm-Wave massive MIMO systems.  
**Author Contributions:** Conceptualization, A.A.-A. and Y.S.; methodology, A.A.-A. and Y.S.; software, A.A.A.; validation, A.A.A., Y.S., S.O. and S.A.; formal analysis, A.A.A.; investigation, A.A.A. and Y.S.; resources, A.A.A.; data curation, A.A.A.; writing--original draft preparation, A.A.A.; writing--review and editing, A.A.A. and Y.S.; visualization, A.A.A.; supervision, M.A.I. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was fully funded by a scholarship for [PERSON] from the Saudi Arabian Cultural Bureau in the UK.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable for studies not involving humans.  
**Data Availability Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# GSFedSec: Group Signature-Based Secure Aggregation for Privacy Preservation in Federated Learning
## 5 Performance Analysis
### Computation Overhead
#### 5.1.3 Computational Cost for Multiple Iterations  
To demonstrate the real-time efficiency of the algorithms, we show the computational cost over multiple iterations. This is computed (as described above) by changing the values of the variables; this is sensitive to the number of iterations. Hence, Table 6 presentsthe dynamic values of the computational costs for various FL iterations. Suppose that this is denoted by \(n\); \(n\) varies between 50 and 1000 and has two intermediate values: 100 and 150. We assume that all iterations use the ElGamal algorithm for cryptography [30]. The encryption and decryption costs for the respective \(n\) were taken from [31].  
For our proposed algorithm, the cost for \(n\) iterations can be computed as the formula given in Equation (22):  
\[CCost_{(Proposed,n)}=C_{\sigma}+C_{VER}+n(C_{ENC}+C_{DEC}+C_{AGG}). \tag{22}\]  
Hence,  
\[CCost_{(Proposed,50)}\] \[\quad=27.947+36.125+50\times(103.78+55.582+1.612)\] \[\quad=8112.772\text{ ms}=8.11\text{ s},\] \[CCost_{(Proposed,100)}\] \[\quad=27.947+36.125+100\times(217.85+102.37+1.612)\] \[\quad=32,247.272\text{ ms}=32.25\text{ s},\] \[CCost_{(Proposed,150)}\] \[\quad=27.947+36.125+150\times(347.89+145.40+1.612)\] \[\quad=74,299.372\text{ ms}=74.30\text{ s},\] \[CCost_{(Proposed,1000)}\] \[\quad=27.947+36.125+1000\times(5210+1060+1.612)\] \[\quad=6,271,676.072\text{ ms}=6271.67\text{ s}.\]  
For comparison, we can compute the computational costs of other popular algorithms similarly, as presented in the following subsections.
## 3 Design  
### System Technical Requirements  
The polarization imager is deployed at the Earth-Moon system's Lagrange point L1. The primary task is to accurately determine the spatial coordinates of the L1 point and analyze the field of view requirements of the optical system accordingly. As a special position on the Earth-Moon line, the L1 point is the point where the gravity of the Earth and the Moon reaches a dynamic balance with the centrifugal force generated by the relative motion, and provides a stable platform for the scientific load to be fixed with the relative position of the Earth and the Moon. Under the assumption that the Earth is an ideal sphere, the half field of view (FOV) required for the overall observation of the Earth from the L1 point can be determined by the spatial geometric relationship between the observation point and the celestial body shown in Figure 3. Specifically, the half-field angle refers to the angle between the light reaching the edge of the imaging surface and the main optical axis starting from the main optical axis of the lens. According to Equation (7), we can calculate the minimum half-field angle required to observe the Earth's edge from point L1 to ensure that the optical system design can meet the specific field coverage requirements.  
\[\sin\ \omega=\frac{R_{E}}{L} \tag{7}\]  
where \(R_{E}\) is the radius of the Earth, \(\mathrm{R_{E}}=6371\) km; L is the distance from the Earth-Moon L1 point to the \(L=0.85d\), and \(\mathrm{d}\) is the average distance of the Earth-Moon, \(\mathrm{d}=384403.9\) km. According to the analysis and calculation, under ideal conditions, assuming that the Earth is a perfect sphere, the minimum half-field angle required for the global measurement of the Earth from the L1 point of the Earth is \(1.12^{\circ}\). Under the condition of actual orbit operation, the observation system needs an uncertainty of about \(0.1^{\circ}\). In the case of meeting the minimum field of view of the Earth's total observation and reserving a sufficient margin, the half field of view of the system is finally determined to be \(1.25^{\circ}\) and the full field of view is \(2.5^{\circ}\). The accurate analysis of the spatial position of the L1 point and the scientific calculation of the field of view of the optical system can provide a solid theoretical basis and technical support for the deployment and operation of the polarization imager.  
\begin{table}
\begin{tabular}{c c c} \hline
**Band Center** & **Bandwidth** & **Main Purposes** \\
**(nm)** & **(nm)** & **Aerosol fine mode size distribution, cloud screening and characterization, vegetation \\
670 & 20 & Clouds, aerosols, vegetation \\
865 & 40 & Clouds, aerosols, vegetation, and surface characteristics \\ \hline \end{tabular}
\end{table}
Table 2: _Cont._  
Figure 3: Geometric relationship of observation points.
# QoS-Aware Downlink Traffic Scheduling for Cellular Networks with Dual Connectivity
## 2 Related Works
### Traffic Scheduling in Dual Connectivity Networks
From the above analysis, we can see that the proposed QDD offers the most complete solutions for the target network.
# Clustering Analysis of Wind Turbine Alarm Sequences Based on Domain Knowledge-Fused Word2 vec
## 2 Alarm Data Description and Statistical Analysis
### Statistical Analysis of Alarm Data  
In the alarm system used in this paper, a total of 568 alarms are configured. These alarms are classified based on their monitored sub_assembles and functions. In Figure 1, we present statistics for the number of alarms configured in the system for each category, as well as the percentage of times each category of alarm is triggered. The right y-axis represents the number of alarms configured for each category, with the number indicated by red crosses. Among them, the top three categories in terms of the number are power supply, cabin, and pitch and blades, with quantities of 118, 110, and 73, respectively. The left y-axis represents the percentage of times each category is triggered. The top three categories are cooling, pitch and blades, and operational state. Upon examining the detailed alarm information, it is found that frequent triggering of repetitive alarms occurs when the coolant hydraulic pressure is low in the cooling system, resulting in a higher alarm proportion. Alarms in the operational state category are related to wind turbine status transitions and mostly consist of information alarms. The higher proportion of alarms related to the pitch and blades reflects the higher frequency of pitch system and blade failures.  
For the 24 wind turbines, we separately calculated the average daily alarm trigger number, as shown by the orange bar in Figure 2. Then, based on the fault time recorded in the maintenance records, we calculated the average daily alarm trigger number for wind turbines when faults occurred, as shown by the blue bar in Figure 2. It can be observed that the average daily alarm trigger number is significantly higher when faults occur compared to normal conditions. This indicates that alarm data serve as strong indicators of wind turbine faults. Additionally, we calculated the maximum daily alarm trigger number for each wind turbine, denoted by red asterisks in Figure 2. The top three values for the maximum daily alarm trigger numbers are 2225, 1172, and 1079, respectively. Handling such a large number of alarms poses a significant challenge for on-site personnel. Additionally, for the entire wind farm, we have calculated the percentage of daily alarm  
Figure 1: The number of alarms configured and the percentage of times triggered for each category.  
trigger numbers within different ranges, as shown in Figure 3. It can be observed that, on average, there are 36.97 days in a year where the wind farm has 10 to 50 alarm triggers. On average, there are 17.56 days in a year where the wind farm has more than 50 alarm triggers. Therefore, effective methods for analyzing alarm data can significantly increase the efficiency of on-site maintenance personnel.
# Wikidata Support in the Creation of Rich Semantic Metadata for Historical Archives
## 3 Mapping from the Wikidata Semantic Model to HERO
### Mappings between Categories  
In this section, we describe the approach we developed to find the Wikidata categories, which correspond to HERO classes or individuals. The strategy that we devised is based on label matching. Since HERO is provided with both Italian and English labels, the matching process relies on both languages. The label-matching strategy consists of a sequence of steps, starting with a class or individual from the HERO ontology to end up with the category in Wikidata which has the same meaning as the input class. In particular, for each HERO class or individual, we perform: (1) Label Extraction, (2) Knowledge Base Access, and (3) Scoring.  
In Section 6, we will discuss ontology matching approaches and why we decided to define a new algorithm instead of using existing tools.  
(1) Label Extraction.The Label Extraction step is aimed at extracting the labels for English and Italian for the HERO category provided as input; then, the extracted lexicalizations are used to access external knowledge bases in the second step. The intuition underlying the labels extraction step is to exploit both languages to improve the precision of the matching strategy. The extraction process for the HERO category (either a class or an individual) \(C\) produces the two sets \(L^{C}{}_{em}\) and \(L^{C}{}_{it}\) of the English and Italian labels, respectively, which are associated with \(C\). In order to increase the precision of the matching strategy, we build the Cartesian product \(L^{C}=L^{C}{}_{em}\times L^{C}{}_{it}\) containing all the associations of each English label with each Italian label for the HERO category C.  
We introduce an example to illustrate the strategy. A graphical representation of the whole process is depicted in Figure 5. Let us consider the HERO _Questioning_ class. In HERO, _Questioning_ is provided with the two sets of labels: \(L^{Questioning}{}_{em}\) = [_questioning_, _interrogation_]and \(L^{Questioning}_{it}=\{interrogatorio\}\) (where _interrogatorio_ is the Italian translation of _questioning_). Thus, the Cartesian product \(L^{Questioning}=\{<interrogation, interrogatorio>,<questioning, interrogatorio>\}\) is the output of this step.
## 2 Motivation and Scenario
\begin{table}
\begin{tabular}{p{142.3 pt} p{142.3 pt}} \hline \hline
**Threat Type** & **Identified Threat** \\ \hline Spoofing [24] & An attacker falsifies the identity of an asset. It can be used to insert a counterfeit part in the supply chain linking it to an existing SCM record, or to send illegitimate data or modify data records, respectively. \\ \hline Tampering [24] & Data records are intentionally inaccurate. An attacker sends false information about the ownership of the asset or its certificates, or because the data records at rest are modified. \\ \hline Repudiation [24] & An attacker disputes a recorded data’s authorship or a legitimate data modifications’ authorship. \\ \hline Information disclosure [24] & An external entity accesses data traffic or data records. \\ \hline Denial of Service [24] & An attacker makes the SCM inoperable temporally or permanently. \\ \hline Elevation of Privileges [24] & A stakeholder uses its privileges to access/modify data that this stakeholder should not be allowed or should be allowed only when it physically owns the asset. \\ \hline System misuse [25] & Several stakeholders collaborate to ignore the verification of the assets in order to include counterfeits, or the validation of the asset is not performed systematically due to lack of concern of the organizations or the employers. \\ \hline \hline \end{tabular}
\end{table} 
Table 1: High-level threats related to the cyber–physical link.
## 3 Results and Discussion
### Adsorbents Characterization
Textural properties for all materials were obtained from the N\({}_{2}\) adsorption-desorption isotherms and are summarized in Table 2. Significant increases in the specific surface areas (S\({}_{\text{BET}}\)) for the Si-PILC with regard to the natural clay are observed, reaching nine times the NC value for the Si-PILC 50. This last is related to the increase in the microporosity of these materials resulting in 57, 65, and 61% for the Si-PILC 25, 50, and 75, respectively. The S\({}_{\text{BET}}\) and micropores volume (V\({}_{\text{HP}}\)) values increased from Si-PILC 25 to Si-PILC 75 and Si-PILC 50, respectively, and it is associated with the amount of micropores present in their structures. The fact that Si-PILC 75 showed lower values than Si-PILC 50 could be due to the amount of pillars agent added. The results suggest that Si/clay ratio higher than 50 mol Si.g\({}^{-1}\) could generate more pillars density within the clay interlayer which during the heating treatment could collapse, decreasing the microporosity. The V\({}_{\text{T}}\) values supported the behavior of the isotherms at high relative pressures obtaining the highest value for the Si-PILC 50. \begin{table}
\begin{tabular}{c c c c} \hline  & **S\({}_{\text{BET}}\) (m\({}^{2}\).g\({}^{-1}\))** & **V\({}_{\text{T}}\) (cm\({}^{3}\).g\({}^{-1}\))** & **V\({}_{\text{HP}}\) (cm\({}^{3}\).g\({}^{-1}\))** \\ \hline Natural Clay & 67 & 0.10 & 0.01 \\ Si-PILC 25 & 485 & 0.30 & 0.17 \\ Si-PILC 50 & 585 & 0.34 & 0.22 \\ Si-PILC 75 & 519 & 0.31 & 0.19 \\ \hline \end{tabular}
\end{table} 
Table 2: Textural properties for the materials. (PILC: pillared clays.) Figure 3: N\({}_{2}\) adsorption–desorption isotherms for the four materials. The PSD for the PILC and NC were studied in the micropores region (pores size below 2 nm) and are shown in Figure 4. As can be observed, the microporosity in the natural clay is considerably lower than that obtained for the pillared clay, which is in accordance with the textural results. All pillared clays have micropore sizes between 0.5 and 2 nm, whereas Si-PILC 25 and 50 showed similar distributions with micropore sizes approximately 0.75 nm. Si-PILC 75 shows contribution of smaller pore sizes (near to 0.7 nm) than the other PILC, suggesting the greater pillar density within the interlayer due to the high Si/clay ratio.