esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Tests and Results
### Temperature Test  
A heat gun is used for the temperature sensor, and the values obtained by the sensor and an infrared thermometer are compared. Table 7 shows the results obtained.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**Imbalanced Load 1**} & \multicolumn{3}{c}{**Predetermined Difference**} & \multicolumn{3}{c}{**Imbalanced Load 2**} & \multicolumn{3}{c}{**Predetermined Difference**} \\ \hline
**No.** & **X** & **Y** & **Z** & **X** & **Y** & **Z** & **X** & **Y** & **Z** & **X** & **Y** & **Z** \\ \hline
1 & _7.77_ & 1.57 & 13.65 & 7.27 & 1.52 & 1.75 & 0.27 & \(-\)4.64 & 16.55 & \(-\)0.23 & \(-\)4.69 & 4.65 \\ \hline
2 & \(-\)4.98 & 1.37 & 11.69 & \(-\)5.48 & 1.32 & \(-\)0.21 & 49.46 & 6.39 & \(-\)1.10 & 48.96 & 6.34 & \(-\)13.00 \\ \hline
3 & 9.06 & 2.35 & 13.89 & 8.56 & 2.30 & 1.99 & \(-\)4.20 & \(-\)9.57 & 3.61 & \(-\)4.70 & \(-\)9.62 & \(-\)8.29 \\ \hline
4 & 5.06 & \(-\)2.79 & 12.59 & 4.56 & \(-\)2.84 & 0.69 & 32.28 & 34.95 & 12.36 & 31.78 & 34.90 & 0.46 \\ \hline
5 & \(-\)6.55 & \(-\)2.94 & 8.20 & \(-\)7.05 & \(-\)2.99 & \(-\)3.70 & 13.22 & \(-\)3.04 & \(-\)2.24 & 12.72 & \(-\)3.09 & \(-\)14.14 \\ \hline Less & & & & 8.56 & 2.30 & 1.99 & & & & 48.96 & 34.90 & 4.65 \\ \hline High & & & & \(-\)7.05 & \(-\)2.99 & \(-\)3.70 & & & & & \(-\)4.70 & \(-\)9.62 & \(-\)14.14 \\ \hline \hline \end{tabular} Note: Each measurement in this table is the average from 50 data points. The default data with the motor off are X = 0.50, Y = 0.05, Z = 11.90.  
\end{table}
Table 6: Axis vibration test.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**No.** & **Thermometer (\({}^{\circ}\)C)** & **Temperature Sensor (\({}^{\circ}\)C)** & **Error** \\ \hline
1 & 21 & 20.90 & 0.48\% \\ \hline
2 & 23 & 23.30 & 1.30\% \\ \hline
3 & 25 & 25.50 & 2.00\% \\ \hline
4 & 27 & 26.90 & 0.37\% \\ \hline
5 & 29 & 30.40 & 4.83\% \\ \hline
6 & 30 & 31.10 & 3.67\% \\ \hline \hline \end{tabular}
\end{table}
Table 7: Temperature sense test.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Test** & **1** & **2** & **3** & **4** & **5** & **Error** \\ \hline L1 current sensor (A) & 1.64 & 1.66 & 1.67 & 1.65 & 1.68 & \\ \hline L1 multimeter (A) & 1.70 & 1.74 & 1.72 & 1.71 & 1.76 & 3.8\% \\ \hline Error & 3.5\% & 4.5\% & 2.9\% & 3.5\% & 4.5\% & \\ \hline L1 current sensor (A) & 1.61 & 1.62 & 1.65 & 1.60 & 1.65 & \\ \hline L1 multimeter (A) & 1.59 & 1.60 & 1.62 & 1.59 & 1.63 & 1.2\% \\ \hline Error & 1.3\% & 1.3\% & 1.9\% & 0.6\% & 1.2\% & \\ \hline L1 current sensor (A) & 1.63 & 1.76 & 1.79 & 1.79 & 1.77 & \\ \hline L1 multimeter (A) & 1.58 & 1.70 & 1.72 & 1.74 & 1.71 & 3.4\% \\ \hline Error & 3.2\% & 3.5\% & 4.1\% & 2.9\% & 3.5\% & \\ \hline \hline \end{tabular}
\end{table}
Table 5: Current test.
# Not All Seeds Are Important: Fuzzing Guided by Untouched Edges
## 4 Design of UntouchFuzz
### Untouched Edges Tracking
Initially, in lines 2-6, the algorithm employs AFL's native edge coverage-based instrumentation, concurrently capturing the random IDs allocated to each basic block. Subsequently, from lines 8 to 27, the algorithm performs to instrument for untouched edges. To elucidate further, lines 10-12 of the algorithm determine whether the current \"untouch_inst1\" is invoked. \"untouch_inst1\" appends instrumentation codes to the beginning of each basic block. The primary function of these codes lies in fetching the value of the global variable \"_afl_bb_ids_\" within the program. It subsequently calculates the ID for the untouched edge per Equation (2) and leverages this ID to update the untouched edge bitmap \"_afl_untouch_ptr\". Figure 4: Example of an AFL-instrumented program’s CFG. ```
Input: A control flow \(G=(BB,E)\) Output: A new control flow \(G^{\prime}=(BB^{\prime},E^{\prime})\)
1\(BB_{IDs}\leftarrow\emptyset,G^{\prime}\leftarrow\emptyset\)for\((BB_{\_},\_)\in G\)do
2 // perform AFL's raw instrumentation
3 BB, ID \(\gets afl\_inst(BB)\) // ID is assigned by AFL for each basic block
4 BB\({}_{IDs}\gets BB_{IDs}\cup(BB,ID)\)
5 end for
6 // traverse all basic blocks (our modification)
7 for\((BB,E)\in G\)do
8 // if the current basic block is not the start basic block of G
9 if\(BB\
eq G\_firstBB()\)then
10 BB \(\gets untouch\_inst1(BB)\)
11 end if
12 _afl_bb_ids \(\gets 0\) // traverse all successor basic blocks of the current basic block
13 for\(BB_{\_}\_succ\in Successors(BB)\)do
14 for\((BB\_t,BB_{ID})\in BB_{IDs}\)do
15 // find the ID of the successor basic block
16 if\(BB\_succ=BB\_t\)then
17 \(ID\gets BB_{ID}\)
18 break
19 end if
20
21 end for
22 _afl_bb_ids \(\leftarrow\_afl\_bb\_ids\oplus ID\)
23 end for
24 BB \(\leftarrow untouch\_inst2(BB,\_afl\_bb\_ids)\)
25\(G^{\prime}\gets G^{\prime}\cup(BB,E)\)
26 end for return\(G^{\prime}\)
```  
**Algorithm 2** Instrumentation for untouched edges  
Moving to lines 15-24, the algorithm traverses the two successor basic blocks of the current basic block, performing XOR operation on the IDs allocated to these two basic blocks. Finally, in line 25, the function \"untouch_inst2\" is invoked. The primary aim of the code is to update the value of the global variable \"_afl_bb_ids\" with the control flow graph, setting it to the outcome of the XOR operation mentioned earlier. At this point, all steps of the untouched edge instrumentation algorithm have been completed. At the low assembly level, a basic block always has exactly two successor basic blocks. However, when we move up to higher-level compiler intermediate languages, it is not guaranteed that a basic block has always exactly two successor basic blocks, especially in programs that contain Switch statements. Specific solutions to this issue will be provided in Section 5. Additionally, it is worth noting that the instrumentation approach proposed still leads to the problem of edge index collisions, where different edges in the CFG are assigned with the same index value [28].
# AI Training Data Management for Reliable Autonomous Vehicles Using Hashgraph
## 4 Proposed System
### System Overview  
To ensure the integrity and security of training data in autonomous vehicle systems, we propose a blockchain-enhanced framework that integrates Hedera for verifying data before it is used for AI model training. The overall process consists of the five following sequential stages:  
* Step 1: Data Collection: Onboard sensors, such as cameras and LiDAR, collect environmental data from the vehicle's surroundings. This process constitutes a part of the standard data acquisition pipeline.
* Step 2: Hashing and Logging: In our proposed system, the collected data are hashed using a cryptographic function (e.g., SHA-256), and the resulting hash value is logged on the Hedera network. This step introduces decentralized integrity validation and is represented in red in Figure 2. The use of Hedera ensures that the fingerprint of the original data is tamper-proof and time-stamped across a distributed ledger.
* Step 3: Data Transmission: The raw sensor data are simultaneously transmitted to a centralized server for storage. This step adheres to the conventional AI training workflow.
* Step 4: Verification Before Training: Prior to model training, the system recomputes the hash of the locally stored data and compares it to the original hash retrievedfrom Hedera. A match confirms that the data have not been altered since the initial logging. This verification module--also highlighted in red--acts as a gatekeeper for maintaining data integrity.
* Step 5: Model Training: If the integrity check is successful, the validated data are passed to the AI training module. The model then learns from trustworthy inputs, ensuring the reliability of downstream predictions.  
Cryptographic hashes are inherently non-reversible, meaning that the hash value alone cannot be used to reconstruct the original data. Therefore, Hedera does not serve as a data storage platform; rather, it serves as a decentralized integrity validation mechanism. This design choice minimizes storage overhead while maximizing verifiability. Through combining real-time data acquisition with immutable, ledger-based verification, the proposed system enhances the trustworthiness of the AI training pipeline. It ensures that only authenticated and untampered data are used during model training, thereby improving both the safety and performance of autonomous driving systems.
## 4 Results and Discussion
### The Relationship between Fatigue Properties and Hardness Profiles  
The difference in fatigue properties of T-welded and cruciform welded joints can also be analyzed from a material perspective [40]. Research has shown that fatigue properties of a material are related to hardness [18; 41; 42; 43]. In general, the fatigue properties increase with increasing hardness. In this study, the relationship between fatigue properties and hardness of welded Dh36 steel at \(-60\)\({}^{\circ}\)C is discussed.  
For the measurement at \(-60\)\({}^{\circ}\)C, the specimen was placed in a low environment chamber and incubated at \(-60\)\({}^{\circ}\)C for 1 h. Then, the hardness of the specimen was measured using a Vickers hardness tester. The surface temperature of the specimen was monitored throughout the measurement process. If the temperature changed by more than 5 \({}^{\circ}\)C, the measurement was invalid. The positions of the measurement points were determined by referring to \"GB/T 2654-2008 Hardness Test Method on Welded Joints\" [44]. The distribution of the measurement points in this study is shown in Figure 12.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \multirow{2}{*}{**Welded Joints**} & **The Hot-Spot** & **The Nominal** & **The SCF by** & **The Hot-Spot** & **The Nominal** & **The SCF by** \\  & **Stress by** & **Stress by** & **FEM** & **Stress by** & **Stress by** & **Tests** \\  & **FEM/MPa** & **FEM/MPa** & **Tests/MPa** & **Tests/MPa** & **Tests/MPa** & **Tests**/MPa** \\ \hline T-welded joints & 342.188 & 312.5 & 1.095 & 352.656 & 313.468 & 1.125 \\ Cruciform welded joints & 370.522 & 313.47 & 1.182 & 387.5 & 315.179 & 1.24 \\ \hline \end{tabular}
\end{table}
Table 6: Comparison of stress concentration factors between the finite element method (FEM) and tests.  
Figure 12: Vickers hardness measurement locations.  
Since the failure zone was located at the HAZ in this paper, the Vickers hardness in the HAZ of the T-welded and cruciform welded joints is mainly discussed here. The Vickers hardness profiles of the T-welded and cruciform welded joints at \(-60\,\mathrm{\SIUnitSymbolCelsius}\) are presented in Figure 13. It can be seen that the Vickers hardness in the HAZ of the T-welded joints is higher (178 HV) than that of the cruciform welded joints (163.8 HV).  
In general, Vickers hardness and fatigue properties show good consistency, that is, without considering inclusions and defects, plastic deformation preferentially occurs at the position with low Vickers hardness and high stress, and the specimen eventually fails in this zone. In this study, the Vickers hardness of the T-welded joints was determined to be approximately 14.2 HV higher than that of the cruciform welded joints in the failure zone. Meantime, the stress range level of T-welded joints in the failure zone was lower than that of cruciform welded joints, as shown in Figure 14. Therefore, the difference in fatigue properties between T-welded and cruciform welded joints can be attributed to the difference in hardness and stress range level of the failure zone in this study.
## 4 Importance of a Flat Pressure Sensing Surface  
The ISP probe used in the whole-mount excised larynx experiment was an early prototype that embedded the Mikro-Cath pressure sensors in an epoxy that minimally covered the sensing elements of each sensor. Each Mikro-Cath pressure sensor is housed in an ovoid capsule with a recessed surface due to its primary application in a fluid environment as an arterial blood pressure sensor. When used in the present application for sensing direct mechanical contact of vocal fold tissue, inserting the embedded pressure sensor between two contacting surfaces produced difficulties in capturing the true contact pressure. Recent work using silicone vocal fold models and a ground-truth load cell has pointed to the importance of embedding each pressure sensor such that the sensing surface coming into contact with vocal fold tissue is as flat as possible for accurate pressure measurement [10]. For example, in that study, the physical size of the sensor caused the vocal fold model to deform around the sensor housing, producing a localized increase in pressure at the location of the sensor and thus over-estimating the true contact pressure. Conversely, the recessed position of the sensing element resulted in an under-estimation of the contact pressure, as the contacting material deformed down into the recessed region to contact the sensing element; materials of varying stiffnesses yielded different behaviors.  
The results of the silicone vocal fold model study [10] were critical in informing the improvement of the ISP probe. The ISP probe manufacturing process was revised to ensure a flat surface at the pressure-sensing end of the probe. To achieve a flat surface for the ISP probe, the sensors were embedded in medical grade room-temperature-vulcanizing (RTV) silicone and the silicone was covered with a thin 0.125 mm silicone sheet. A glass histology slide was lightly clamped against the sheet until the silicone hardened, resulting in a flat surface with the sensor elements lying immediately beneath. The added silicone layer was sufficient to fill the recessed space (air gap) and act as an appropriate surface with which the opposing vocal fold tissue in the hemilarynx model could come into contact. A flat surface above the pressure sensing element was critical to guard against significantly increased uncertainty in measurements with any recessed space or excess silicone spilling out of the space.
# Constant Transmission Efficiency Dimming Control Scheme for VLC Systems
## 5 Discussion  
We have done much research on the dimming control function of VLC systems in different scenes. Paper [12] proposed a dimming control scheme based on multilevel parity check codes (ML-PCC) for multilevel transmission. A dimming control scheme based on multi-LED phase-shifted space-time codes (MP-STC) were proposed for multi-LED VLC systems in paper [23]. Paper [24] provided a dimming control scheme based on constant weight space-time codes (CWSTC) for Multiple Input Multiple Output (MIMO) VLC systems. In this paper, we propose the ECWCS scheme for the dimmable VLC systems with  
Figure 4: Spectral efficiency of variable on-off keying (VOOK), variable pulse position modulation (VPPM) and ECWCS.  
constant transmission efficiency and a low complexity decoding algorithm for the ECWCS scheme. In addition to this, it can also provide a wider dimming range, a smaller division value, and better error performance compared with the previous constant transmission efficiency dimming control scheme [14]. We think that the ECWCS scheme is a better choice for dimmable VLC systems with constant transmission efficiency.
# Checked and Approved? Human Resources Managers' Uses of Social Media for Cybervetting
## 2 Literature Review
### The Importance of Visual and Textual Cues for Cybervetting  
The way in which information that is accessible via SNS influences the decision making by HR professionals might be similar to cues that influence them during traditional job interviews, such as their looks and their writing skills.  
As far as face-to-face contact is concerned during a job interview, studies found that the physical appearance of job seekers influence HR professionals' assessment [26]. People who are found attractive are evaluated more positively (based on the heuristic: what's beautiful is good) [27; 28]. Physically attractive individuals benefit from their looks when evaluated for a job [26; 29]. This halo effect has been linked with personality factorsrelated to productivity, such as self-confidence [30]. The importance of a candidate's physical appearance is also illustrated by a qualitative study in which HR-professionals were asked to speak about a candidate they just met. When describing the candidate, physical characteristics were often first enumerated. Their discourse centered around embodied capability where bodily fitness of candidates is interpreted as a reflection of their competencies and qualities [31]. SNS not only offer HR professionals the possibility to see (and assess) the physical attractiveness of a candidate based on a (well-chosen) profile photo, but also by other pictures that appear on the individual's timeline, pictures wherein the person is tagged and other group photographs that are less controllable by an individual. Therefore, it is important for job seekers who are active on SNS to assess how their profile picture (and other publicly accessible pictures) could be interpreted by potential employers. Having no profile picture at all is not an option. A profile lacking a face picture leads to doubts about the profile's credibility. Having a 'face pic' secures the 'existence' of the profile owner [21, 22].  
Next to visual cues about a candidate, textual information is also assessed, which provides HR professionals with an indication of the writing skills of job applicants [16]. By comparing this unconstrained everyday writing to the more formal style in a candidate's cover letter and resume, recruiters may gain a deeper insight into the writing capacities of the candidate. In addition to writing style, also the content of the writing matters. Frequency of writing about certain topics may be a proxy for personal interests. The use of pejorative terms or other offensive language may also signal certain values [32]. Additionally, recruiters may also check the networks (friend lists and contact lists) that job seekers have on their social media profiles, in order to assess their social capital and the size of their professional networks. Some recruiters also evaluate how applicants interact with other social network users, as an informal confirmation of their social competencies [33].
## 2 System Modeling
### Model of Components
#### 2.2.7 Solar PV  
For installed solar PV panels, power generated by the PV mostly depends on solar irradiance and ambient temperature. In reality, the forecast values of PV generation witness fluctuations due to uncertain solar radiation [39], thus accurate information of the PV generation for a future time is unpredictable. Therefore, the PV generation is stochastic and is an uncertain quantity. In this paper, the p-efficient point method is applied to determine the optimal amount of variable PV generation based on historical data at given confidence levels and then incorporates it into the MEMG optimization problem. A more detailed explanation of the proposed method is provided in the next section.
## 3 Results
### Results of Synthesis  
#### 3.3.1 MT vs. Control  
Due to the heterogeneity of both outcome measurements and interventions, the meta-analysis could only be conducted for the VAS, comparing before and immediately after the intervention. Data were extracted from five included studies [30; 32; 33; 35; 36] to analyze the VAS scale. Two studies showed a significant effect in favor of the intervention group [30; 33]. The overall effect across all studies showed a moderate effect in favor of the MT intervention (effect size = 0.799; \(Z\) = 2.495; \(p\) = 0.013; IC 95%= 0.171 to 1.427; Q-value = 17.721; \(p\) = 0.001). Table 1 shows the effect size (standard error, 95% CI) and weight for each study included in the meta-analysis. For publication bias, a Funnel plot is shown in Figure S1.  
Figure 4 shows the forest plot graphs for both the effect size (95% CI) for each study and the overall results.  
Four studies [22; 26; 27; 28] also assessed knee PPT immediately after the intervention, but used different protocols. Three studies [30; 34; 35] showed an improvement (\(p\) < 0.05) between the MT and the control group after the intervention. However, [PERSON] et al. [36] found no differences. Regarding the distal PPT, [PERSON] et al. [30] found an improvement in the PPT shoulder in the MT group compared to the control intervention (212, 95%CI 136,288). [PERSON] et al. [35] also found a difference (\(p\) < 0.05) between the MT intervention and the control group in PPT elbow. Some outcomes were only assessed in one study. The NPRS improved (\(p\) < 0.05) in the MT group compared with the control group [31], as did the WOMAC pain subscale (\(p\) < 0.05) [33]. On the other hand, no differences were found for the WOMAC [30].  
Regarding follow-up, VAS was evaluated in three studies [30; 33; 35]. The VAS improved at two days [30] but not both at one week [35] or six weeks [33]. The knee PPT showed differences (\(p\) < 0.05) between the MT group and the control intervention [30; 34], but no differences were no found after four days [34; 35]. The same results were found for the shoulder [30] and elbow PPT [34; 35]. [PERSON] et al. [30] found no differences between the groups for the WOMAC after 2 days of the intervention. No differences were found on the WOMAC pain subscale at 16 and 24 weeks [33].  
The evidence using GRADE is shown in Table A4. Overall, it is very low due to the small number of studies and the imprecision and high heterogeneity of the results. Only the post-intervention VAS showed high quality of evidence.
# Drag and Attitude Control for the Next Generation Gravity Mission
## 4 Drag and Attitude Control Impacts on Mission Performance
### Implications on the Gravity Field Retrieval
Even though the different cases do not substantially affect the maximum resolution, they do affect the accuracy of the low degrees, by over 1 order of magnitude below degree 40. This may not seem relevant to the observability of the HIS signal, but such a large difference may play an important role in the estimation of the low degrees, with consequences to diverse geophysical applications. For example, the C\({}_{20}\) coefficient of GRACE is notoriously difficult to estimate, and some studies have hinted that the cause is the poor performance of the accelerometers at low frequencies [39]. Furthermore, the low-quality data provided by GFO's accelerometer have created the need to have an independent estimate of the C\({}_{30}\) coefficient [40].
## 3 Results
### Anti-Noise Performance  
The received Alpha navigation signal in space will carry noise, which will reduce the SNR of the signal, thus affecting signal detection. In general, Alpha receivers are required to be able to detect signals with an SNR greater than \(-10\) dB. Therefore, we generate signals with \(-30\) dB \(<\) SNR \(<0\) dB to obtain the detection probability of the detection method under different SNRs.  
Figure 8 shows the signal detection probability under different SNRs. When the SNR is greater than \(-7\) dB, the detection probability is \(100\%\). Although the performance of the detection method decreases faster when the SNR is greater than \(-10\) dB, the detection probability of the signal is exceeds \(90\%\). Therefore, it is proven that the proposed method has good anti-noise performance. The proposed detection method can fulfill the requirement of Alpha navigation signal detection by receiver.  
Figure 7: Notch filter results and signal detection results. (**a**) \(f_{1}\) signal obtained via notch filter. (**b**) \(f_{1}\) signal detection results. (**c**) \(f_{2}\) signal obtained via notch filter. (**d**) \(f_{2}\) signal detection results. (**e**) \(f_{3}\) signal obtained via notch filter. (**f**) \(f_{3}\) signal detection results.