esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experimental Evaluation  
### Datasets and Preprocessing  
#### 4.1.1 Large PE Malware Dataset  
This malware corpus is from [47] which is significantly large containing more than 222,700 Windows PE malware of over 500 different classes such as Trojan, Virus, Worm, Backdoor and so on. The samples are mainly downloaded from VirusTotal [48] and class tags are mostly manually labeled. Due to labeling errors, this dataset can be noisy. To filter the noise, we use an unsupervised learning method called DBSCAN [49] to detect the noisy ones among the samples tagged with the same class label and only take out samples in the same cluster to form the class. We think that wrongly tagged samples can be viewed as noise which can be separated from correctly tagged samples by DBSCAN in some extent and we further adopt the method extracting PE meta-data in [26] to get shallow representation of malware samples which will be input to DBSCAN. The minimum points in a cluster is set to 20 forcing each cluster to satisfy the scale requirement and we randomly take 20 samples out of a random cluster for each class to form a filtered dataset referred as \"filtered LargePE\". There are 208 classes in filtered LargePE and we take 100 classes out of them as meta-training set, 58 classes as meta-validating set, 50 classes as meta-testing set.  
Figure 4: Best probabilities of SoftMax classifier using cosine distance.  
Without doubt, this kind of preprocessing will make this dataset easier to fit, but also suffice to distinguish the performance of various models in malware few-shot classification. The unfiltered dataset referred as \"unfiltered LargePE\" is also used to test the robustness of models in a noisy scenario and there are 300 classes in meta-training set, 57 classes in meta-validating set and 50 classes in meta-testing set in unfiltered LargePE.
## 3 Results
### Effect of Refereeing Technique and Competition Sex on Total Game Workload Demands  
The number of referees strongly influenced physical demands, showing high effect sizes for total distance (\(F\) = 134.71; \(p<0.001\); \(\omega_{p}\)\({}^{2}\) = 0.61), distance \(>\) 16 km/h (\(F\) = 76.00; \(p<0.001\); \(\omega_{p}\)\({}^{2}\) = 0.44), SpeedAVG (\(F\) = 58.35; \(p<0.001\); \(\omega_{p}\)\({}^{2}\) = 0.38), impacts \(>\) 8 g (\(F\) = 28.19; \(p<0.001\); \(\omega_{p}\)\({}^{2}\) = 0.24), PL (\(F\) = 22.64; \(p<0.001\); \(\omega_{p}\)\({}^{2}\) = 0.20), and DecMAX (\(F\) = 20.34; \(p<0.001\); \(\omega_{p}\)\({}^{2}\) = 0.19). SpeedMAX showed moderate effects (\(F\) = 6.91; \(p\) = 0.01; \(\omega_{p}\)\({}^{2}\) = 0.06) with higher values in 2-referee teams. No effects were found in AccMAX, %HRMAX, and time above 90% of HRMAX (\(F\) < 1.18; \(p>0.28\); \(\omega_{p}\)\({}^{2}\) = 0) based on the number of referees.  
The sex of games showed small effects on distance \(>\) 16 km/h (\(F\) = 4.73; \(p\) = 0.03; \(\omega_{p}\)\({}^{2}\) = 0.02) and SpeedAVG (\(F\) = 4.45; \(p\) = 0.04; \(\omega_{p}\)\({}^{2}\) = 0.02), with higher values in the female competition. No effects were found in other variables (\(F\) < 2.95; \(p>0.15\); \(\omega_{p}\)\({}^{2}\) = 0). For this reason, the interaction between the refereeing technique and game sex showed small effects only for distance covered \(>\) 16 km/h (\(F\) = 6.29; \(p\) = 0.01; \(\omega_{p}\)\({}^{2}\) = 0.03) and SpeedAVG (\(F\) = 5.34; \(p\) = 0.02; \(\omega_{p}\)\({}^{2}\) = 0.03).  
Figure 2 illustrates the pairwise comparisons between the game sex and refereeing technique. Teams with two referees showed very large effects for total distance in both sexes (\(t>\) 7.27; \(p<0.001\); \(d>\) 2.27) and distance \(>\) 16 km/h and SpeedAVG in the male competition (\(t\) = 7.27-8.11; \(p<0.001\); \(d\) = 2.27-2.42). Large effects were found for distance \(>\) 16 km/h in the female competition (\(t\) = 4.30; \(p<0.001\); \(d\) = 1.34) and impacts \(>\)8 g in the male competition (\(t\) = 4.30; \(p<0.001\); \(d\) = 1.34). A moderate effect size was observed for DecMAX and PL in both competitions (\(t\) = 3.15-3.46; \(p<0.01\); \(d\) = 0.94-1.08) and SpeedAVG and impacts \(>\) 8 g in the female competition (\(t\) = 3.02-3.69; \(p<0.02\); \(d\) = 0.94-1.15). Based on the competition sex, 3-referee teams obtained higher values in the female competition  
for distance \(>\) 16 km/h and Speed\({}_{\rm AVG}\) with a moderate effect size (\(t\) = 2.97-3.15; \(p\) \(<\) 0.02; \(d\) = 0.95-1.01).  
Figure 2: Descriptive and inferential analysis regarding the effect of refereeing technique and sex of basketball competition on internal and external workload demands. _Note:_ 3, statistical differences with 3-referee teams (\(p\) \(<\) 0.01); \(\dot{\sigma}\), statistical differences with the male competition. [PERSON]’s \(d\) effect size: ** moderate, *** large, *** very large.
## 3 Evaluation Experiments
### Objective Evaluation Experiments on Frequency Characteristics  
In this experiment, we measured the frequency characteristics in the sound fields synthesized with the comparison patterns. The impulse responses at the observation points were measured with a TSP signal [30] at 0-8 kHz to calculate the frequency characteristics. The logarithmic spectral difference (LSD) was utilized for evaluation, which can be given as  
\[\text{LSD}_{\text{pt}}(\mathbf{r}) =\sqrt{\frac{1}{F_{f}}\sum_{f}\biggl{(}10\log_{10}\frac{\mathcal{O }_{\text{pt},\mathbf{r}}[f]}{\mathcal{O}_{\text{Real},\mathbf{r}}[f]}\biggr{)}^{2}}, \tag{30}\] \[\mathcal{O}_{\text{pt},\mathbf{r}} =\mathbf{W}_{\text{DFT}}\sigma_{\text{pt},\mathbf{r}}, \tag{31}\]  
where \(F_{f}\) denotes the number of frequency indices for evaluation, \(\sigma_{\text{pt},\mathbf{r}}\) denotes the observation signal at r in the time domain, and \(\mathbf{W}_{\text{DFT}}\) denotes the DFT matrix. The frequency range for evaluation was set to 0-8 kHz, which is the same as the TSP signal utilized in measurement. A smaller value of LSD indicates a better reproduction of the frequency characteristics. It can be noticed that the value of LSD is non-negative; therefore, we calculated the LSD at each observation point and then averaged these values across the entire sound field.  
The results on LSD are given in Table 4. Values in bold font indicate the best performance among the comparison patterns. It can be concluded from Table 4 that the proposed method achieved the lowest LSD overall. Moreover, the performance of the proposed method was robust against the position of the VSS.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Methods** & \(\mathbf{r_{5}}\) = (0.0, 0.5) & \(\mathbf{r_{5}}\) = (0.0, 1.0) & \(\mathbf{r_{5}}\) = (0.0, 1.5) & \(\mathbf{r_{5}}\) = (0.0, 2.0) & **Average** \\ \hline Baseline & 8.8 & 8.4 & 8.4 & 8.6 & 8.6 \\ \hline Conv. PAL & 8.5 & 8.9 & 8.8 & 8.9 & 8.8 \\ \hline Conv. Hybrid & 8.8 & 8.4 & 8.8 & 8.9 & 8.7 \\ \hline Prop.(8.8) & 7.5 & 8.1 & **7.0** & 5.3 & 7.0 \\ \hline Prop.(16.16) & **7.1** & **7.3** & 7.4 & **5.2** & **6.7** \\ \hline \hline \end{tabular}
\end{table}
Table 4: Results on logarithmic spectral difference [dB].  
Figure 8: Results for sound pressure distribution for \(\mathbf{r_{5}}\) = (0.0, 2.0).
# HISEA-1: The First C-Band SAR Miniaturized Satellite for Ocean and Coastal Observation
## 2 Typical Data and Preliminary Results
### 3D Features of High Rise  
The area in the red rectangle on the right of Figure 3 is the Siming Campus of Xiamen University. Buildings, lakes, and playgrounds are also shown in the image. Twin towers are observed at the lower-right corner of the right rectangle. The 3D features of some high-rises were shown in the red ovals, in which the bottom is dark and top is bright. Based on the phase information, the quantitative geometric features could be obtained further.
## 4 Analysis of Economical Structural Framing
### Additional Material Cost Analysis  
Second, the length of the system channel required to construct the PMR structural frames was calculated (Figure 7). The average construction cost per square meter in South Korea from 2015 to 2022 (when the case study was conducted) was approximately 1500 USD (Korea Public Procurement Service), and the unit price of the applied system channel per meter was 15 USD. Even if only one meter of system channel is used for a 1-square-meter rack, the construction cost increases by 1%. The cost of electrical equipment construction alone increases further. Hence, the PMR structural frames significantly impact the total construction cost, and, therefore, this study proposed an economical way of constructing the structural frames. In this study, the input length of the system channel per cubic meter of the plenum space was calculated to analyze the economic feasibility of each PMR-structural-frame design.  
In Type A frames, the supporting interval of structural frames was fixed at 1.25 m. Accordingly, the system channel per cubic meter was the highest: 6.75 m. When calculated with respect to a 1-square-meter area, a system channel of length 4 m and an additional 60 USD were required as input. This corresponded to an increase of 4% in the total construction  
Figure 6: Design cost analysis (person-day input for design phase).  
cost. Type B and Type C were found to be the most economical alternatives in terms of material input because the supporting interval of the structural frame was set to 2 m.  
The input lengths of the system channels required for Type B and Type C were 4.13 m and 4.55 m, respectively, per cubic meter. In Type D, where the frames were attached to the concrete surface of the ceiling, the supporting interval was adjusted to 2 m; however, the input system- channel length was 6.61 m per cubic meter, similar to Type A. In Type E, the supporting interval was set to 1.5 m. This was predicted to be the most economical as the upper system channel layer was removed; however, in terms of the actual material input, the system channel input was similar to that of Type B. Both methods, adjusting the supporting intervals of structural frames (Type B) and designing structural framing only on the necessary areas (Type E), caused a reduction in the material cost.
## 2 Materials and Methods
### Material Synthesis for the Laboratory-Prepared Targets  
The laboratory-prepared targets were elaborated using Mg-doped or Zn-doped GaN powders, whose synthesis processes were reported by our research group in previous works ([PERSON] et al. [16; 17; 19]). A brief description of the process used to obtain Mg- or Zn-doped GaN powders is provided. To synthesize the Zn-doped GaN powders, 7.477 g (107.20 mmol) of metallic gallium (99.999% pure), and 0.075 g (1.15 mmol) of metallic zinc, which was approximately 1% in the mixture, were used as reagents. The synthesis of Mg-doped GaN powders, 5.874 g (84.24 mmol) of metallic gallium, and 0.059 g (2.44 mmol) of metallic magnesium, which was also approximately 1% in the incorporation, were used as reagents. Additionally, anhydrous ammonia (NH\({}_{3}\)) was used as the source of nitrogen atoms in both processes. The processes begin by placing the metallic gallium and metallic zinc (or metallic magnesium) in an alumina boat, which was preheated on a hot plate at 200 \({}^{\circ}\)C and two hours of manual agitation was performed. After this time, the obtained metallic liquid solution was placed inside a CVD furnace, which was purged with an N\({}_{2}\) flow of 150 sccm (processed at room temperature), to reduce the residual oxygen. Then, an N\({}_{2}\) flow of 50 sccm was opened, and the temperature was increased until reaching 20 \({}^{\circ}\)C above the fusion temperature of the doping element, which was carried out to ensure the diffusion of the Zn atoms (or Mg atoms) in the gallium by supersaturation of the liquid solution. Table 1 presents the fusion temperature of the doping elements. Once the homogenization temperature was stabilized (Table 1), the liquid solution was homogenized for 14 h in an N\({}_{2}\) flow of 50 sccm. Then, the N\({}_{2}\) flow was closed, and an NH\({}_{3}\) flow of 150 sccm was opened to make the nitridation process; the temperature was then increased to 1000 \({}^{\circ}\)C, where the liquid solution remained for two hours. When this time elapsed, the temperature was decreased to 600 \({}^{\circ}\)C, where the NH\({}_{3}\) flow was closed, and the N\({}_{2}\) flow of 50 sccm was opened again, while the temperature continued decreasing until it reached the room temperature, ending the process. Thus, using this process, 8.34 g (164.49 mmol) of Zn-doped GaN powders were synthesized, with a nitrogen incorporation of 0.786 g (56.14 mmol) after the nitridation. Additionally, 6.34 g (115.65 mmol) of Mg-doped GaN powders were also synthesized, with a nitrogen incorporation of 0.405 g (28.97 mmol) after the nitridation process.
## 2 Theoretical and Conceptual Framework
### Cybersecurity in the Automotive Sector
#### 2.1.2 Roles in the Automotive Cybersecurity Ecosystem  
The automotive cybersecurity process involves multiple stakeholders, each playing a critical role in ensuring vehicle security:  
* Vehicle Manufacturers (OEMs): OEMs are responsible for designing and implementing cybersecurity measures at the vehicle level. This includes securing software, hardware, and communication systems to protect against cyber threats.
* Automotive National Authorities: National regulatory bodies establish cybersecurity standards and compliance frameworks that OEMs and other stakeholders must adhere to. These authorities ensure that vehicles meet the necessary cybersecurity requirements before they are allowed on the road.
* Compliance Assessment Bodies: These independent organizations assess whether automotive systems and components comply with established cybersecurity standards. They conduct audits and evaluations to verify the security posture of vehicles and associated infrastructure.
* Infrastructure Managers: Infrastructure managers are responsible for securing the external systems that vehicles interact with, such as traffic management systems, road infrastructure, and cloud services. They play a critical role in ensuring that vehicle-to-infrastructure (V2I) communication remains secure.
# Binary-Tree-Fed Mixnet: An Efficient Symmetric Encryption Solution
## 4 Security Analysis
### Security Model  
In order to obtain a security measure of the scheme, some starting assumptions must be established:  
1. The bit length of keys (\(k_{ij}\)) and labels (\(L_{ij}\)) is large enough to reduce the probability of guessing them from their XOR encryption forms to a negligible value.
2. Peer-to-peer communications are safe; in other words, adversaries cannot prevent (DoS), decrypt (confidentiality), impersonate (authentication), or tamper with (integrity) these communications.
3. Adversaries can only gather information from their role in the process or from other adversaries.
## 3 Results and Discussion  
### Pre-Wind Tunnel Evaluations  
After the full assembly of the morphing wing, including the 3D-printed wing box, embedded ETMs, internal actuation system and flexible skin, the feasibility of the deformation was tested for its various magnitudes. Two main goals must be achieved: 1--ensuring the smooth deformation of the ETMs; and 2--maintaining the flexibility of the skin, particularly in the transition sections, where twist deformations occur.  
Prior to testing the full assembly, each critical component was evaluated independently, which included examining the deformation behavior of the ETMs and the performance of the transition section. Figure 16 illustrates an ETM after undergoing deformation.  
Figure 14: Graphical user interface for controlling the deflection of the SMTE flap.  
Figure 15: Electrical diagram of the actuation system.  
As shown in Figure 16, the skin deforms smoothly without any signs of buckling on the wing lower surface, which is attributed to the pre-tensioning applied to the skin prior to its attachment to ETM, as discussed in Section 2.5. Furthermore, the deformation of the ETM was smooth and aligns very well with the results obtained from the structural analysis presented in [37].  
Another critical component of the SMTE flap system is its transition sections. These sections were located between the solid wing box and the ETM on the left and right sides of the SMTE flap. The transition sections were covered with flexible skin, which plays a vital role in connecting the morphing section to the solid wing box. Due to this connection, twist morphing occurred in the transition sections, making the flexible skin essential for accommodating the deformation while maintaining its structural integrity. Additionally, the twist morphing in the transition sections also affected the flaps' solid edges. To mitigate the deformation and reduce stress concentrations at the flaps' solid edges, two triangular flexible rubber components with the same cross-section as the flap edge were incorporated. These rubber components effectively moderated the deformation and enhanced the durability of the solid edges. Figure 17 illustrates the experimental setup for testing the transition section with its corresponding components, while Figure 18 depicts the transition sections before and after morphing.  
As illustrated in Figure 18a, the connecting rubber components are incorporated along the solid edge. These components effectively smooth out the deformation of the transition section, as shown in Figure 18b. While a simple solid connection, over time, would increase the likelihood of failure in the transition sections, incorporating these flexible connections mitigates this risk by accommodating deformation. Furthermore, Figure 18b also shows that the flexible skin exhibits smooth and consistent behavior throughout the morphing process, enhancing the overall reliability of the system.  
Figure 16: ETM after deformation.
# Reduction of Hexavalent Chromium Using _Sorbaria sorbifolia_ Aqueous Leaf Extract
## 3 Results
### Possible Mechanism for Reduction Efficiency of PLE  
The FTIR spectra of PLE sample is recorded in Figure 8d, which clearly shows absorption band at 3307, 1636, 1040 and 612 cm\({}^{-1}\), suggesting the presence of -OH group [32], C=O group [33], C-O group [34], stretching C-H group bending frequencies.  
The plant extracts usually contain sugars, glycosides, phenolics and even vitamins. Hydroxyl groups from sugars and phenolics can affect the reduction of metal salts including also vitamins [35; 36; 37; 38; 39; 40; 41; 42]. Aerial parts of _Sorbitaria sorbifolia_ are known to contain a variety of polyphenols, flavonoids and glycosides [23; 24; 25]; glycosides are non-reducing sugars, but polyphenols and flavonoids are known antioxidants that serve as reducing agents [36; 37; 38; 39] and are thus most likely responsible for the reduction  
\begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Reductants** & \begin{tabular}{l} **Initial Cr(VI)** \\ **Concentration** \\ \end{tabular} & \
\begin{tabular}{l} **pH** \\ **Time** \\ \end{tabular} &
\begin{tabular}{l} **Reduction** \\ **Efficiency** \\ \end{tabular} & **References** \\ \hline Ferrous chloride (300 \(\mu\)M) & 50 \(\mu\)M & 7.2 & 120 min & 100\% & [10] \\ Citrate (10 mM) & 192.32 \(\mu\)M & 2.25 & 24 h & 12\% & \\ Citrate (10 mM) + Mn(II) (182.02 \(\mu\)M) & 192.32 \(\mu\)M & 2.25 & 24 h & 52\% & [12] \\ Citrate (10 mM) + clay (3% (\(\mu\)/\(\
u\))) & 192.32 \(\mu\)M & 2.25 & 24 h & 84\% & \\ Citrate (10 mM) + clay (3% (\(\mu\)/\(\
u\))) + Mn(II) (182.02 \(\mu\)M) & 192.32 \(\mu\)M & 2.25 & 6 h & 100\% & \\ Oaxial acid (300 \(\mu\)M) & 50 \(\mu\)M & 3 & 20 min & 100\% & [13] \\ Ascorbic acid (300 \(\mu\)M) & 100 \(\mu\)M & 7 & 120 min & 100\% & [14] \\ Taratric acid (12 mM) + Isopropyl alcohol (0.29 M) & 2.0 mM & 4 & 99 h & 19\% & \\ Russet soil (5 g) & 2.0 mM & 4 & 99 h & 25\% & [15] \\ Russet soil (5 g) + Tartaric acid (12 mM) + Isopropyl & 2.0 mM & 4 & 99 h & 99\% & \\ alcohol (0.29 M) & 50 mg/L & 2.73 & 2 days & 96\% & [31] \\ _Punic gramatum_=2 YVI (Fe(0) 1.53 mM) & 10 mg/L & 2–10 & 60 min & 99–66\% & Present study \\ PLE (1.5 mL) & & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of results using various reductants.  
Figure 7: Effects of initial Cr(VI) concentration: [Cr(VI)]\({}_{0}\) = 5–80 mg-L\({}^{-1}\), Cr(VI) volume = 25 mL, contact time = 60 min, pH = 2, PLE quantity = 1.5 mL.  
of Cr(VI) to Cr(III) using 'greener' PLE as a reductant. A schematic presentation delineates the reduction mechanism (Figure 8a-c); captured image for the gradual conversion of carcinogenic Cr(VI) into nontoxic Cr(III) is shown in Figure 8e.
# Case Study of Expected Loss Failure Mode and Effect Analysis Model Based on Maintenance Data
## 2 Research Methodology
### Data Definition and Modeling Procedure
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multicolumn{4}{c}{**(b)** Intermediate variables} \\ \hline
**Variable** & **Description** & **Formulation** \\ \hline \(N\) & Number of regular maintenances & \(N=\frac{T}{\tau}\) & \\ \(N_{C}\) & Number of cause failures & & observation value \\ \(N_{S}\) & Number of system failures & & observation value \\ \(\tau_{D}\) & Average system downtime & \(\tau_{D}=\frac{1}{N_{S}}\sum\limits_{i=1}^{N_{S}}(\tau_{i,2}-\tau_{i,1})\) & \\ \(\tau_{U}\) & Average system uptime & \(\tau_{U}=\frac{T}{N_{S}}-\tau_{D}\) & \\ \(\lambda_{C}\) & Cause failure rate & \(\lambda_{C}=-\frac{\ln(1-N_{C}/N)}{N^{\tau}}\cong\frac{N_{C}}{N^{\tau}}\) & [see Equation (1)] \\ \(\lambda_{S}\) & System failure rate & \(\lambda_{S}=-\frac{\ln(1-N_{S}/N)}{N^{\tau}}\cong\frac{N_{S}}{N^{\tau}}\) & [see Equation (2)] \\ \(\alpha\) & Maintenance cost of a cause failure & \(\alpha=n_{C}\times\pi_{C}^{\tau}\) & observation value \\ \(\beta\) & Maintenance cost of a system failure & & \\ \(\theta\) & Loss per unit time due to system breakdown & \(\theta=n_{\text{people}}\pi_{\text{people}}+n_{\text{prod}}\pi_{\text{prod}}\) & \\ \hline \multicolumn{4}{c}{**(c)** Output values} \\ \hline
**Variable** & **Description** & **Formulation** \\ \hline \(L\) & Expected loss for \(k\)-causes system & \(L=T\sum\limits_{i=1}^{k}\left[\lambda_{C}\alpha_{i}+\lambda_{S}(\beta_{i}+ \theta\tau_{D_{i}})\right]\) [see Equation (4)] \\ \(L_{\text{sens}}\) & Expected loss \(k\)-causes system with detection sensors & \(L_{\text{sens}}=T\sum\limits_{i=1}^{k}\left(\lambda_{C},\alpha_{i}+\lambda_{S} \alpha_{i}+\lambda_{\text{sens}},\pi_{\text{sens}i}\right)\) [see Equation (6)] \\ \(m=\frac{L_{\text{sens}}}{L}\) & : install detection sensor for \(m<c_{1}\), \\ \(m\) & Alternative coefficient & do not install detection sensor for \(m>c_{2}\), \\  & & hold off otherwise. \\  & & \(c_{1}=0.8\) and \(c_{2}=1.2\), for instance. \\ \hline \hline \end{tabular}
\end{table} 
Table 1: _Cont._ \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Failure Modes** & **Operational** & **Failure** & **Failure** & **Compensating** & **Remarks** \\
**and Causes** & **Mode** & **Effects** & **Method** & **Provisions** & **Remarks** \\ \hline - & - & - & - & - & - \\ \hline \hline \end{tabular}
\end{table} 
Table 2: FMEA format of MIL-STD-1629 (*). Table 1. By performing the procedure in Part (b), as shown in Part (c) of Table 1, two types of expected loss and the alternative coefficient can be calculated. The detailed calculation procedure is described in the next Section 2.2.