esa-sceva/satcom-chunk-collection huggingface dataset:
# Intelligent Dynamic Data Offloading in a Competitive Mobile Edge Computing Market
## 6 Results
### Operation of the DO-MECS Framework
#### 6.1.1 Homogeneous End-Users
Furthermore, from Equation (4), we observe that the reputation score \(R_{s}\) depends on the relative pricing, the congestion and the penetration of the MEC servers. The \(R_{s}\) essentially controls the probability based on which each end-user will select a server to offload its data. In Figure 3e, the results illustrate that the proposed DO-MECS framework tried to boost \"weaker\" servers to allow them to gain some traction on the market. Additionally, Figure 3f presents the profit \(P_{s}^{(t)}(\mathbf{b}^{(t)},\mathbf{p}^{(t)})\) that each MEC server received based on its price announcement and the end-users' data offloading. The results reveal that Server 1 achieved the highest profit due to the combined effect of having the lowest cost (Table 1) and attracting a large number of end-users, despite the fact that it presented the lowest price, as shown in Figure 3a. The same trend was followed from the rest of the servers, which indicates that the announced price by the MEC server was not the only dominant factor in shaping the server's profit, but also the number of end-users that selected to be served by a server was a key parameter in determining the server's overall profit.
# Gaze Tracking Using an Unmodified Web Camera and Convolutional Neural Network
## 5 Conclusions  
In this study, a possibility to track a person's gaze using an unmodified camera has been investigated. Since commercial eye tracking devices are expensive, their availability is limited. In this research, the low-cost eye-tracking method was proposed which can be easily used for standard desktop or laptop computers without needing any additional equipment. The state-of-the-art CNN network with unmodified webcams commonly available with computers was used. The images acquired from the unmodified cameras are low quality and very sensitive to changing light conditions. Therefore, it was a big challenge to achieve reasonable results. However, this study shows that when the CNN network is used with a carefully designed topology and tuned hyper-parameters, it is possible to achieve results that may be usable in practical applications in a real-world environment.  
Although this study achieved some significant results, there are some limitations associated with this study as well. For instance, calculating the exact gaze point was not treated as the regression problem but simplified into classifying gaze point as belonging to one of 20 areas, which makes it difficult to compare with other methods. Furthermore, the number of participants and the amount of data were limited, while it is well known that deep learning networks work and generalize better when trained using huge datasets. Moreover, this study did not include any publicly available datasets to perform the benchmark on them.  
In our future research investigations, we plan to address the limitations mentioned above. We will consider extending our dataset and testing our model by using publicly available datasets of eye positions.  
[PERSON] was responsible for preparing the state of the art, elaborating the results, and preparing most of the text; [PERSON] was responsible for the initial idea, network architecture, results analysis, and supervision; [PERSON] was responsible for the implementation and performing all calculations. All authors have read and agreed to the published version of the manuscript.  
This work was supported by Statutory Research funds of Department of Applied Informatics, Silesian University of Technology Grant No: 02/100/BKM21/0016.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement**: Not applicable.  
**Data Availability Statement**: The dataset used in the research will be available on GitHub repository upon acceptance of the paper.  
**Conflicts of Interest**: The authors declare no conflicts of interest.
## 2 Measurement Theory  
The measurement theory of DA-OA-ICOS: when a laser beam passes through a stable optical resonant cavity filled with gas, the gas molecules will absorb the light at a specific wavelength resulting in the attenuation of the light intensity. The gas volume fraction \(C\) in the cavity can be expressed as [28]:  
\[C=\frac{N}{N_{T}}=N\frac{P_{0}T}{N_{L}PT_{0}}=\frac{AP_{0}T}{N_{L}PT_{0}SL}, \tag{1}\]In Equation (1), \(N\) is the molecular density of the gas absorption medium; \(N_{\rm T}\) is the total number of molecules per cubic centimeter; \(N_{\rm L}\) is the Loschmidt number; \(P_{0}\) is the gas pressure under the standard condition; \(T_{0}\) is the gas temperature under the standard condition; \(P\) and \(T\) are the pressure and temperature in the optical resonance cavity, respectively; \(A\) is the absorption integral area of the absorption spectroscopy; \(S\) is the line intensity; \(L\) is the effective optical path. It can be obtained that the direct absorption integral area is proportional to the gas concentration.  
The measurement theory of WM-OA-ICOS: according to the Beer-Lambert law, when a light beam with the frequency of \(\
u\) and the light intensity of \(I_{0}\) through a substance with thickness of \(L\) (i.e., effective optical path), the absorption in the substance will lead to the attenuation of the transmitted light intensity. The transmitted light intensity \(I(\
u)\) can be expressed as follows:  
\[I(\
u)=I_{0}e^{-\sigma(\
u)NL}, \tag{2}\]  
In Equation (2), \(\sigma(\
u)\) is the absorption cross section of the absorbing molecule. If a signal with frequency \(\omega\) is used to demodulate the laser current with output center frequency \(\
u_{0}\), the instantaneous frequency of the output signal is \(\
u=\
u_{0}+\delta_{\
u}\)cos \(\omega t\). And \(\delta_{\
u}\) is the demodulated signal amplitude. The output signals were further processed by the Cosine Fourier Series expansion. The harmonic component of the level number \(A_{n}\) is then obtained [29]:  
\[A_{n}(\
u_{0})=\frac{2I_{0}NL}{\pi}\int_{0}^{\pi}-\sigma(\
u_{0}+\delta_{\
u} \cos\theta)\cos n\theta d\theta, \tag{3}\]  
In Equation (3), \(\theta=\omega t\) and the amplitude of the harmonic component is proportional to the concentration of the absorbing molecule when the effective absorbed optical path is kept constant.  
For an OA-ICOS with a cavity lens distance of \(d\) and a cavity lens reflectivity of \(R\), the effective optical path \(L\) can be expressed as  
\[L=\frac{d}{1-R+\alpha(\
u)d}, \tag{4}\]  
In Equation (4), \(\alpha(\
u)\) is the absorption coefficient. When the absorption intensity is weak and the one-way absorption of light in the cavity satisfies \(1-R>>\alpha(\
u)\), \(L\) can be expressed as  
\[L=\frac{d}{1-R} \tag{5}\]  
It can be concluded that the effective optical path is constant and the optical resonance cavity can be equated to a multi-pass cell. The concentration can be inverted on the direct absorption integral area or the harmonic signal amplitude.
# Numerical Modeling of Engineering-Scale Jointed Coal Mass and Confining Pressure Effect
## 3 Model Construction of Engineering-Scale Jointed Coal Mass  
### Parameter Statistics and Modeling of Joints in Coal Mass  
This section first introduces the first step of the model construction method described above. The tailgate at the 1714 working face of Honghui Coal Mine (with a coal wall size of 5.0 m \(\times\) 3.5 m) was selected as a sampling object to characterize the joint distribution in the coal mass. The distribution of joints in the coal seam in the 1714 working face was featured with the following characteristics. (a) The coal seam was widely distributed with approximately parallel bedding. (b) The coal seam contained cleats perpendicular to the direction of the laminations, which cut the coal mass into non-continuous approximate rectangular laminar blocks. (c) There were few crossings between adjacent bedding and cleats.  
The statistics of the joint distribution in the coal mass revealed that the spacing of the joints in the coal seams was generally between 10 and 25 cm, and the length of the joints was related to their types. The number of beddings gradually decreased with the increase in the spacing, and exhibited a negative exponential distribution, with a mean and standard deviation of 14.2 cm and 2.9 cm, respectively. The statistics of the cleats exhibited a log-normal distribution, with a mean and standard deviation of 17.5 cm and 4.7 cm, respectively.  
To streamline the numerical model, this paper simplifies the bedding and cleats into smooth joints. Specifically, the bedding is represented as horizontal in the model, while the cleats are depicted as perpendicular to the bedding. For scenarios where the joint distribution parameters are obtained, the DFN creation function in PFC2D can be employed to accurately position the bedding and cleats. The numerical model of the jointed coal mass could be constructed by labeling the particles through which the DFN passed and replacing the labeled particles in the contact model, as illustrated in Figure 1. The bedding and cleats embedded in the model are distributed according to the above statistical results.
# IoT Firmware Emulation and Its Security Application in Fuzzing: A Critical Revisit
## 6 Bug Detection Capability of Emulator's Fuzzing Application
### False Positives  
False positives in firmware fuzzing with emulators often result from low-fidelity peripheral emulation, leading to increased manual effort for bug reproduction and confirmation.  
Low fidelity in peripheral emulation increases false positives. For instance, feeding fuzzing test cases to peripheral hardware responses can lead to false crashes. Tools like _Fuzzware_ and _Ember-IO_ might trigger peripheral interrupts before peripheral initialization and even feed fuzzing test cases to hardware-generated MMIO registers reading, such as SRs, causing uninitialized function pointers in interrupt service routines (ISRs) to lead to false crashes. In comparison, fine-grained peripheral models, which incorporated additional hardware information like _SEmu_ and _Perry_, have relatively low false-positive rates. Similarly, _FirmWire_ and _MetaEmu_, designed for specific applications, rely on manual configuration and tailored test constraints, resulting in fewer false positives. The accuracy of fuzzing enhancements relies on the fidelity of their emulators.
## 4 General Discussion
### Experimental Implications and Limitations
It is important to note that fracture analyses and characterisation, based on thin section analyses (Section 3.1 and Table 7) and micro-CT scanning (Section 3.6 and Figure 10), was done under atmospheric conditions. Hence, it does not represent properties (e.g., fracture aperture) at subsurface conditions. Stress release, during the unloading phase at the end of the UCS test conducted on calcschist TD12-CS4, presumably affected the detected apertures in Figure 10 [62]. Most probably, at reservoir conditions, the apertures observed in Figure 10 will become smaller due to elevated stress levels. Finally, in Section 3.7, the effect of increasing temperature on the seismic velocity was investigated. The experimental procedure, as described in Section 2.5, involves placing the samples in the oven for at least 24 h and, subsequently, conducting the active-source acoustic measurement at a room temperature of 22 \(\pm\) 1 \({}^{\circ}\)C. Though the measurement process for a single sample only took 30 s, within that time frame the rock sample, and especially its surface, cooled down according to [PERSON]'s law of cooling [63; 64]. When assuming a cooling constant of 7 \(\times\) 10\({}^{-3}\) s\({}^{-1}\) (based on a heat transfer coefficient of 100 W/m\({}^{2}\)/K [65] and a heat capacity of 0.851/g/K [66]), in combination with an oven (i.e., initial) temperature of 240 \(\pm\) 1 \({}^{\circ}\)C, the rock's surface would cool down to approximately 225 and 200 \({}^{\circ}\)C after 10 and 30 s, respectively. However, the temperature within the rock itself would most likely still be close to the initial temperature set.
## 3 Comparison and Validation of the Proposed Calculation Method
### Validation 3  
[PERSON] et al. [35] conducted large-scale shaking table tests to investigate the seismic earth pressure on retaining walls. The input vibration was a 5 Hz sinusoidal wave, with the maximum acceleration amplitude being incrementally increased by approximately 50-100 Gal until significant displacement of the retaining wall occurred. The test configuration comprised a 0.60 m high retaining wall with a vertical interface between the wall and backfill soil. The backfill material was Inagi sand, which was placed horizontally behind the wall. Key computational parameters are listed in Table 1, where it is shown that Case 3 considered cohesive backfill soil and Case 4 adopted cohesionless backfill (cohesion = 0).  
An amplification factor fa = 1.1 was incorporated in the calculations. The pseudo-dynamic method assumes that the backfill soil reaches a limit state; thus, residual strength was adopted in the computations to better align with practical conditions.  
A comparison between the computational results of the proposed method and experimental data is presented in Figure 7. The results are consistent with the experimental observations. However, when \(k_{\text{h}}\) < 0.7, the shaking table test results exceed the computational values. This discrepancy may arise because the experimental conditions did not fully attain the assumed active stress state, leading to an overestimated earth pressure. When the input acceleration exceeds 0.7 g, the backfill soil reaches the critical state of active earth pressure in the model tests, resulting in excellent agreement with the computational results.
# Two-State Lasing in Microdisk Laser Diodes with Quantum Dot Active Region
###### Abstract  
The two-state lasing phenomenon, which manifests itself in simultaneous laser emission through several optical transitions of quantum dots, is studied in microdisk diode lasers with different cavity diameters. The active region represents a multiply stacked array of self-organized InAs/InGaAs/GaAs quantum dots emitting in the wavelength range of 1.1-1.3 \(\upmu\)m. Two-state lasing, which involves the ground-state and the first excited-state optical transitions, is observed in microdisks with cavity diameters of 20 to 28 \(\upmu\)m, whereas two-state lasing via the first and the second excited-state optical transitions is observed in 9 \(\upmu\)m microdisks. The threshold currents for one-state and two-state lasing are investigated as functions of the microdisk diameter. Optical loss in the microdisk lasers is evaluated by comparing the two-state lasing behavior of the microdisks with that of edge-emitting stripe lasers made of the same epitaxial wafer.  
two-state lasing; quantum dots; microdisk laser; ground state; excited state +
Footnote †: journal: _Photonics_
# An Optimized Hierarchical Cluster Formation Approach for Management of Smart Cities
## 3 Proposed Approach
### Assumptions  
The present work outfits the LEACH protocol under the following assumptions:  
1. CHs are already elected for each cluster. Thus, the energy model of the proposed work is simpler, especially since the cluster formation step of LEACH starts independently and after the CH is elected.
2. The locations of all nodes are known; thus, the distance between any two nodes and the distance between any node and the BS can be easily calculated using the Euclidean distance equation.
3. The transmitted packets from all nodes are equal in size. Thus, the impact of the proposed model can be studied without an effect of the data size.
4. The BS performs all the calculations in each round and is adequately equipped with the power required for this task. Thus, the power of each node is affected only by the operations of the proposed approach.
5. Each node uses the same energy level and can estimate the residual energy at the BS depending on the power transfer and transmission cost.  
As in LEACH, the CH can originate a time-division multiple access (TDMA) schedule, which decides when the SNs can transmit each time slot. So, no unexpected traffic load can interrupt the transmission of the proposed work.
## 3 Technical Design of GSFR-BLS
### Graph-Based Skeleton Feature Representation
The representation matrix \(R\) captures the selected keypoints' self-information and their adjacency relationships, and this matrix is used as the input to the BLS for further classification. The spatial-temporal information embedded in \(R\) enables the BLS to capture nuanced movement patterns indicative of dangerous driving actions. The procedure for constructing the representation matrix \(R\) in Algorithm 1 dynamically selects key skeletal points based on motion displacement, applies a threshold from the global average movement to filter irrelevant points, updates adjacency matrices, and finally concatenates the normalized identity and adjacency matrices to serve as input to the BLS for efficient and robust feature extraction in dangerous driving action recognition. ```
Input: 3D skeleton keypoint positions \(P_{i}^{t}\) for each frame \(t\), time window \(T\), adjacency matrix \(A\), identity matrix \(I\), degree matrix \(D\), thresholding parameter \(\beta\) Output: Representation matrix \(R\) for BLS input
1 Initialize \(A_{1}\), \(A_{2}\gets A\);
2 foreachframe \(t\) in time window \(T\)do
3 foreach keypoint \(P_{i}\)do
4 Compute displacement \(\Delta P_{i}^{t}=P_{i}^{t}-P_{i}^{t-1}\);
5 Accumulate total movement \(\Delta P_{i}^{\text{total}}=\sum_{k=1}^{T}\Delta P_{i}^{t-k}\);
6
7 end for
8 Compute global average movement \(\bar{\Delta P}^{\text{total}}=\frac{1}{N}\sum_{i=1}^{N}\Delta P_{i}^{\text{ total}}\);
9 Set dynamic threshold \(\theta=\beta\cdot\bar{\Delta P}^{\text{total}}\);
10 foreach keypoint \(P_{i}\)do
11 if\(\Delta P_{i}^{\text{total}}>\theta\)then
12 Mark \(P_{i}\) as an important keypoint;
13
14 end if
15
16 end for
17 Update \(A_{1}\) and \(A_{2}\) by keeping only edges between important keypoints;
18
19 end for
20 Normalize \(A_{1}^{\prime}=A_{1}\cdot D^{-1}\);
21 Normalize \(A_{2}^{\prime}=A_{2}\cdot D^{-1}\);
22 Normalize identity matrix \(I^{\prime}=I\);
23 Concatenate normalized matrices to obtain \(R\): \[R=[I^{\prime},A_{1}^{\prime},A_{2}^{\prime}]\]
24 return\(R\)
```  
**Algorithm 1**Constructing representation matrix \(R\) with spatio-temporal dynamic selection.
## 4 Results and Discussion
### Potential Health Hazards Assessment
#### 4.3.7 Indoor Yearly Effective Dose Owing to Radon Exposure (IYEDRn)  
The IYEDRn in mSv y\({}^{-1}\) incurred by the populations owing to the IRC is computed through the model presented in UNSCEAR [73] is as follows:  
\[\text{IYED}_{\text{Rn}}\Big{[}\text{mSvy}^{-1}\Big{]}=\text{IRC}\Big{[}\text{ Bq m}^{-3}\Big{]}\times F_{1}\times 8766\Big{[}\text{h y}^{-1}\Big{]} \times F_{2}\times F_{3} \tag{11}\]  
where \(F_{1}\) (=0.4), \(F_{2}\) (=0.8), and \(F_{3}\) (=9 \(\times\) 10\({}^{-6}\) mSv per Bq m\({}^{-3}\) h\({}^{-1}\)) are the factors of equilibrium equivalent concentration, individuals' indoor occupancy, and dose conversion, respectively [31; 73].  
The values of the IYEDRn (Table 4 and Figure 13) of the measured samples fluctuated between 1.06 to 4.51 mSv/y with an average value of 2.42 \(\pm\) 0.21 mSv/y, i.e., higher than the worldwide average (1.2 mSv/y) provided by UNSCEAR [21]. However, the IYEDRn values for 30% of the considered samples lie within the (R.A.L.) recommended action level (3-10 mSv/y) stated in ICRP [61], while the remaining 70% are less than this level (Figure 13). As a result, ElSibai-Abu EITiyur granites do not cause possible risks to the population.  
Figure 12: Variation of IRC in the investigated samples.
# A Frequency-Selective Reconfigurable Antenna for Wireless Applications in the S and C Bands
To facilitate portability and effortless placement within the experimental framework, a compact antenna was imperative. Moreover, its multifrequency attributes are essential for comprehensively studying attenuation concerning rain rate across multiple frequencies. The switching mechanism of the antenna which can be easily controlled by a microcontroller is also valuable for this application. This paper is organized as follows. Section 2 describes the antenna geometry. Simulation results are presented in Section 3. Experimental results and discussion are presented in Section 4, followed by the conclusions in Section 5.
# Real-Time Lightweight Morphological Detection for Chinese Mitten Crab Origin Tracing
###### Abstract  
During the cultivation and circulation of Chinese mitten crab (Eriocheir sinensis), the difficulty in tracing geographic origin leads to quality uncertainty and market disorder. To address this challenge, this study proposes a two-stage origin traceability framework that integrates a lightweight object detector and a high-precision classifier. In the first stage, an improved YOLOv10n-based model is designed by incorporating omni-dimensional dynamic convolution, a SlimNeck structure, and a Lightweight Shared Convolutional Detection head, which effectively enhances the detection accuracy of crab targets under complex multi-scale environments while reducing computational cost. In the second stage, an Improved GoogleNet's Inception Net for Crab is developed based on the Inception module, with further integration of Asymmetric Convolution Blocks and Squeeze and Excitation modules to improve the feature extraction and classification ability for regional origin. A comprehensive crab dataset is constructed, containing images from diverse farming sites, including variations in species, color, size, angle, and background conditions. Experimental results show that the proposed detector achieves an mAP50 of 99.5% and an mAP50-95 of 88.5%, while maintaining 309 FPS and reducing GFLOPs by 35.3%. Meanwhile, the classification model achieves high accuracy with only 17.4% and 40% of the parameters of VGG16 and AlexNet, respectively. In conclusion, the proposed method achieves an optimal accuracy-speed-complexity trade-off, enabling robust real-time traceability for aquaculture systems.  
Chinese mitten crab; attention mechanism; YOLOv10; target detection; image recognition; geographic origin traceability