esa-sceva/satcom-chunk-collection huggingface dataset:
# Towards Bio-Hybrid Energy Harvesting in the Real-World:
## 3 Research Progress in Energy Harvesting from Microorganisms
### Impact of Other Micro and Macro Organisms in MFCs  
MFCs utilize catalytic activities of microorganisms on organic substrates to generate electrical energy [18]. While use of electrogenic bacteria is the most common practice [156] for MFCs, examples of using other micro, like fungi [157], and macro organisms, like algae [72] and plants [158] are also reported.  
Many works have attempted to classify microorganisms based on their use in MFCs. For example, [49] classified use of electro-active microorganisms on MFC electrodes into two major classes: bacteria, archaea, eukaryotes on anodes and bacteria, archaea on cathodes. Again, these two major classes are further divided into five classes. While [PERSON] et al. [48] classified microorganisms for external mediator-less configuration that included five classes of Proteobacteria, identified to date, in addition to some microalgae, yeast, and fungi. Here we have classified MFCs based on their anodic biocatalysts into the following three major classes (bacteria based, yeast-based and Archaea-based MFC) as depicted in Figure 8 and this has been abridged from the classification exoelectrogens in [49]. In the review we focused on bacteria-based MFCs and give a short overview of yeast-based MFCs.  
Moreover, impacts of other microorganisms and the presence of other animals also impact in situ MFC performances. [PERSON] et al. [159] hypothesized that population of electroactive bacteria attached on the marine biocathode surface had significant negative impact caused by the intervention of Protozoa, such as amoeba. [PERSON] et al. [160] performed multiple experiments to identify if protozoan grazing can reduce current output from sediment MFCs and identified as important the MFC current output limiting. A similar observation was also made by [PERSON] et al. [77]. [PERSON] et al. [161] reported their works on impact of aquatic worm predation on power generation from activated sludge with MFCs and found that it basically enhanced power output.
# Person Re-Identification across Data Distributions Based on General Purpose DNN Object Detector
## 4 Proposed Method
### Image Databases  
In this paper, we used two image databases, i.e., one for the training phase and one for testing our proposed method. This section presents both of the databases.  
Firstly, we target an image dataset with several identities regarding the training phase, but we also need images with only one person. This is the reason why we have chosen the RPIField database [18] in this stage. This database contains 112 known identities. For each identity from the database, there are 12 camera views available, which is an essential feature of this dataset. Besides the known identities, which appear on each camera, there are also several unknown pedestrians.  
The naming of each folder and each image is very helpful, being easier to generate a set of triplets when there is a well-known rule. The rule for naming the folder is _personID_appearance_, while the rule for naming an image from folders is _CameraNO_FrameNO.png_. For each image from the database, there is an associated.txt file. This file provides the location of the person within the image as well as the label.  
Before training the neural network while using the RPIField database, we went through a database processing step. We propose only keeping the clean images to not introduce the wrong information through the network. Thus, the processing step involves several actions:  
1. eliminating all the images that contain two or more persons;
2. eliminating all the images in which a person is partially hidden by other object, or is not fully captured on camera; and,
3. we eliminate all of the blurred images and the out of focus ones, in order to remove noise from database.  
After this step, we only kept the clear images, which completely capture a person. In Figure 8, some samples that are extracted from the RPIField database are presented.  
From this database, we initially used only 900 images that belonged to 30 people. Those images were randomly chosen from the clean database. After that, we formed the triplets that were used in the second phase of training from this small subset of images. Later in the training stage, we expanded the database to 60 images per subject, forming 1800 images.  
Regarding the testing stage, we first tested our method on a subset of images that were extracted from the same database. Most importantly, we tested it on a completely different database, i.e., PRW-v16.04.20 [19]. This image database contains frames that are captured with six different cameras. Each frame has several persons, which makes the re-identification process more difficult. The images represent crowded places and, inevitably, there are occlusions of people, thus making re-identification impossible in certain situations.  
We used this second database in order to test our system's performances in a different situation, i.e., in a more general scenario. Figure 9 captures several frames that were extracted from this database.
## Appendix A Van der Waals Binding Energy between Few-Layer Graphite Sheets  
Consistent with the continuous-medium theory used to describe the elastic properties of graphene, the Girifalco model [29] is an interesting approach for addressing the Van der Waals cohesion of layered materials. The central idea of the model is to treat each atomic layer as a continuous plane containing \(N\) atoms per unit area with which an external atom interacts through a Lennard-Jones 6-12 potential. If \(d\) is the distance, the interaction energy between the plane and the atom is \(V(d)=b/10d^{10}-a/4d^{4}\), where \(a\) and \(b\) are two positive constants. Considering the external atom as belonging to a graphene plane located at a distance \(h\) above another graphene plane, it follows from the expression of \(V(d)\) that the binding energy per unit area between the two parallel planes is  
\[\sigma_{1,1}=N\left[\frac{a}{4h^{4}}-\frac{b}{10h^{10}}\right] \tag{8}\]  
where the index \(1,1\) means the interaction between a monolayer of graphene and another monolayer of graphene.  
The same procedure can be developed for the interaction energy between an \(n\)-layer graphite sheet and another \(n\)-layer graphite, assuming a regular spacing \(h\) between the successive \(n+n\) atomic planes. Summing up the interactions over the \(2n-1\) inter-plane distances yields the following expression of the interaction energy  
\[\sigma_{n,n}=N\left[\frac{a}{4h^{4}}\left(2\sum_{j=1}^{2n-1}\frac{1}{j^{4}}-1- \frac{1}{(2n-1)^{4}}\right)-\frac{b}{10h^{10}}\left(2\sum_{j=1}^{2n-1}\frac{1} {j^{10}}-1-\frac{1}{(2n-1)^{10}}\right)\right] \tag{9}\]  
At the limit \(n\rightarrow\infty\), the Van der Waals binding energy of graphite is obtained:  
\[\sigma_{\rm Gr}=N\left[\frac{a}{4h^{4}}\left(2\zeta_{4}-1\right)-\frac{b}{10h ^{10}}\left(2\zeta_{4}-1\right)\right] \tag{10}\]  
with \(\zeta_{k}\) the Riemann zeta function of power \(k\). Using the parameters \(a\), \(b\), \(h\), and \(N\) given in Ref. [5], the values listed in Table 2 are obtained. The result for graphite agrees remarkably well with available experimental data [27; 58].  
Equations (8) and (10) readily lead to the relation  
\[\sigma_{1,1}+\sigma_{\rm Gr}=2N\left[\frac{A}{4h^{4}}-\frac{B}{10h^{10}}\right] =\frac{3}{10}\,\frac{NA}{h^{4}}=\frac{h}{20}\,C_{33} \tag{11}\]  
with \(C_{33}\) the elastic constant of graphite along the \(c\) axis, where \(A=a\zeta_{4}\), \(B=b\zeta_{10}\), knowing that \(B/A=h^{6}\) and \(C_{33}=6 NA/h^{5}\)[5].
## 3 Methodology  
With the lap-wise discretized race simulation (described in Section 1) as a basis, there are few alternatives to MCS for evaluating the effects of probabilistic influences. One option would be to use what-if scenarios, for example, \"What happens if the SC gets deployed on lap 30?\". However, this can only be dealt with if we refrain from considering combinations of many probabilistic influences due to the rapidly increasing complexity. Another idea would be to discretize the possible range of the random variables and then simulate races for all combinations (full factorial design). In contrast to MCS, this approach would lead to better sampling of the parameter space in low-probability regions. MCS, on the other hand, provides more meaningful results because it utilizes probability distributions that represent real behavior. Besides, a full factorial design suffers from the curse of dimensionality, which quickly increases computation time. As in literature, MCS is therefore preferred. MCS requires  
Figure 4: Lap times of the drivers on positions 1–3 in Le Castellet 2018. The first laps are affected by a safety car (SC) phase, the last laps by a virtual safety car (VSC) phase. Crosses mark those lap times that are chosen to calculate the average lap time increase during SC and VSC phases. The horizontal lines indicate 140% and 160 % of the fastest lap time of the race \(t_{\text{lap,min}}\), which is used as a basis.  
that the generated random numbers are independent and identically distributed [3] p. 4. Provided that the computer's random number generator (RNG) fulfills this requirement, it also holds for most of the commonly used random distributions, since they are sampled based on the RNG. This also applies to the distributions used in this paper: Gauss distribution [11], Beta distribution [12] and log-logistic (Fisk) distribution [12].  
The following sections describe how we have modeled the influences presented in the previous chapter in order to overcome the mentioned limitations. Damaged cars are not considered as we do not have the necessary data available. Besides, it is a rare case that a car that has been involved in an accident is only damaged so slightly that it can continue the race.
## 4 Tests and Results
### \"End-Loss\" Scenario
\begin{table}
\begin{tabular}{c c c c} \hline
**Scenario** & **Latency (s)** & **STD (vs. K) (ns)** & **STD (vs. R) (ns)** \\ \hline No latency & 0 & — & 0.21 \\ \hline \multirow{4}{*}{A} & 30 & 0.53 & 0.61 \\ \cline{2-4}  & 60 & 0.72 & 0.78 \\ \cline{2-4}  & 90 & 0.95 & 0.95 \\ \cline{2-4}  & 120 & 1.15 & 1.15 \\ \hline \multirow{4}{*}{B} & 30 & 0.48 & 0.53 \\ \cline{2-4}  & 60 & 0.64 & 0.72 \\ \cline{2-4}  & 90 & 0.75 & 0.85 \\ \cline{2-4}  & 120 & 0.87 & 0.93 \\ \hline C & 120 & 0.85 & 0.90 \\ \hline D & 120 & 0.32 & 0.53 \\ \hline \multirow{4}{*}{E} & 30 & 0.13 & 0.28 \\ \cline{2-4}  & 60 & 0.26 & 0.36 \\ \cline{2-4}  & 90 & 0.34 & 0.47 \\ \cline{1-1} \cline{2-4}  & 120 & 0.47 & 0.57 \\ \hline \end{tabular}
\end{table} 
Table 4: The STDs of Sentinel-3B satellite clocks in different scenarios. K represents the reference clocks determined by Kalman filtering utilizing the CNES real-time products, and R denotes another reference clock estimated by using BLS and utilizing the CODE final products. \begin{table}
\begin{tabular}{c c c} \hline
**Prediction Period (s)** & **STD of CNES GPS Satellite Clocks (vs. COM Final Clock) (ns)** & **STD of Sentinel-3B Clocks (vs. R) (ns)** \\ \hline
0 & 0.079 & 0.21 \\ \hline
30 & 0.104 & 0.28 \\ \hline
60 & 0.120 & 0.36 \\ \hline
90 & 0.135 & 0.47 \\ \hline
120 & 0.155 & 0.57 \\ \hline \end{tabular}
\end{table} 
Table 5: The prediction precision of CNES GPS satellite clocks and the precision of Sentinel-3B clocks in scenario E.  
As representative examples, the left panel of Figure 9 shows the MDEVs of the GPS clocks (re-referenced to a stable reference) for G16 (Block IIR) and G30 (Block IIF) before (left panel) and after (right panel) prediction for 120 s. The G16 clocks exhibit a worse short- to mid-term stability than the G30. The right panel of Figure 9 shows the clock estimation errors (without latency) and clock prediction errors (with a latency of 120 s) for these two clocks. It can be seen that while both clocks are precisely estimated, the short-term noise of the prediction errors of G16 are significantly larger than those for G30. This is caused by the worse short-term stability of the G16 clocks, as shown in the left panel of Figure 9.
## 4 Electromagnetic Performance Analysis
### Magnetic Flux Distribution and Flux Linkage Analysis  
The magnetic flux distribution is verified to study the flux behaviour in a multi-tooth HE-DSDPM. Figure 6 presents the open-circuit magnetic flux and field density of the proposed machine with different \(I_{dc}\). As illustrated in the red dashed circle, the strong magnetic flux is crowned at the stator tooth tip, the rotor tip and the air gap at all \(I_{dc}\). This is because the most magnetic flux is produced by slot opening PM. Typically, the magnetic field density at the air gap has more important due to the influence on the magnetic flux path between the stator teeth and rotor poles. It can be seen that the highest magnetic field density at the air gap is about 2.13 T excited by \(I_{dc}\) of \(+5\) A, followed by 1.74 and 0.94 T excited by \(I_{dc}\) of 0 A and \(-5\) A, respectively. As a result, the strengthening flux distribution is achieved by excited at \(I_{dc}\) of \(+5\) A while the wakening flux distribution is achieved by excited at \(I_{dc}\) of \(-5\) A.  
The flux linkage was computed from the magnetic flux distribution for primary investigation. As shown in Figure 7, the open-circuit flux linkage of the proposed HE-DSDPM structures with different \(I_{dc}\) is presented by varying the rotor positions. The multi-tooth HE-DSDPM provides the highest flux linkage of 0.013 Wb, similar to conventional HE-DSPM at \(I_{dc}\) of 0 A. Although the geometry configuration of the multi-tooth HE-DSDPM is modified, the flux linkage keeps the same magnitude because the magnetic flux path is improved.  
When both machines are excited by \(I_{dc}\) of \(-5\) and \(+5\) A, it is found that the highest flux linkage of the proposed structure at \(I_{dc}\) of \(-5\) and \(I_{dc}\) of \(+5\) A has 32.82% smaller and 35.80% larger than that at no field current, respectively. Especially, the multi-tooth HE-DSDPM exhibits a larger variance flux linkage percentage with better flux regulation quality when compared to the conventional one due to the slot area of the winding coil being enlarged by the structural modification of the multi-tooth technique. Then, the behaviour of the magnetic flux distribution and flux linkage will be further utilised to describe the back-EMF and torque production.  
Figure 6: Open-circuit magnetic flux and field density of a multi-tooth HE-DSDPM with various \(I_{dc}\); (a) \(I_{dc}=0\) A, (b) \(I_{dc}=-5\) A, and (c) \(I_{dc}=+5\) A.
# Dynamic Behavior of an Embedded Foundation under Horizontal Vibration in a Poroelastic Half-Space
## 2 Problem Definition and Governing Equations  
### Problem Statement  
The foundations are subjected to time-harmonic loading as shown in Figure 1. The cylindrical foundation is bonded with soil medium with the same displacement, and the contact between foundation and soil is totally permeable. The exact solution for an embedded foundation under dynamic vibration is hard to obtain due to complex mathematics. Thin layer theory by [PERSON] and [PERSON] [24; 25] is adopted to separate the soil medium into two independent parts. The soil underlying the foundation base is assumed to be a homogeneous poroelastic half-space while the soil along the side of the foundation is assumed to be a series of infinitesimally thin layers [20]. The total reaction force consists of the reaction force at the foundation side and foundation base. The vertical and torsional displacement of the foundation is neglected.
## 3 Results
### Mortality Risk Prediction Based on SHAP Interpretation Machine Learning
#### 3.4.1 Interpreting the Model Globally  
(1) Explanation of the Importance of Features.  
The interpretation of XGBoost in the SHAP framework can be achieved by displaying a plot of the positive and negative effects of each feature. As Figure 7. The vertical coordinates in the plot indicate the different features, with each row corresponding to a feature variable. The horizontal coordinates, on the other hand, represent the SHAP values, which indicate the effect on the model output results. Each point in the graph represents a sample, and its color reflects the magnitude of the feature value taken for that sample. The color of the point in the graph is determined by the magnitude of the feature value. Points with larger feature values are redder, while points with smaller feature values are bluer. Additionally, the cross-sectional area of the \"honeycomb\" in the graph is determined by the number of points with the same SHAP value. A larger number of points with the same SHAP value results in a larger cross-sectional area, which presents a coarser visual effect.  
As illustrated in the figure, age is the most significant predictor of mortality risk in patients with primary thyroid lymphoma, followed by the presence of \"B\" symptoms, histological type, and marital status. Furthermore, the figure indicates that age exerts a positive effect on the risk of death, suggesting that the older the patient, the higher the risk of death. In contrast, tumor metastasis and surgery exert a negative effect on the risk of death, indicating that these indicators play an inverse role in the risk of death of the patient.  
(2) The Impact of Two-By-Two Interaction Characteristics on Prediction Outcomes.  
The diagonal portion of the interaction plot illustrates the relationship between the features and the predicted values. As Figure 8. The off-diagonal portion, in contrast, depicts the effect of combining features two by two on the predicted values. The horizontal coordinate of each subplot represents the SHAP value, indicating the degree of significance of the effect of a given combination of features on the results. The greater the width of the subplot, the more pronounced the effect.  
As illustrated in the figure, apart from the diagonal, the combinations of age and \"B\" symptom characteristics had the greatest impact on the predicted outcomes.  
Figure 7: Positive and negative effect plots for each variable in the SHAP framework.
# A System-Level Methodology for the Design of Reliable Low-Power Wireless Sensor Networks
## 2 Sensor Node Energy Model
### Modeling Framework  
The presented modeling framework [36] is based on Simulink/Stateflow and allows the creation of energy consumption models of configurable COTS components, based on FSMs. The main components of a SN taken into consideration are the microcontroller unit (MCU), the transceiver, and the sensor. A Stateflow chart functions as an FSM within the Simulink model. In addition, it is possible to integrate MATLAB functions that can reside anywhere in a Stateflow chart, state, or sub-chart. Therefore, the framework ensures a high degree of modeling flexibility, as shown in Figure 1. It also shows that it represents multiple levels of subcomponents in a system, making multilevel-state complexity of a SN more manageable.  
Figure 1: A general sensor node energy model using Stateflow charts.
# SDR-Based 28 GHz mmWave Channel Modeling of Railway Marshaling Yard
## 3 Software-Defined Radio-Based Channel Modeling
### SDR mmWave Measurement System
#### 3.1.2 Self-Developed Sounding Data Process Software  
The Laboratory Virtual Instrument Engineering Workbench (LabVIEW) platform was employed to develop our self-developed software in order to analyze the collected data. LabVIEW is a programming environment developed by NI company that uses graphical editing G language to write programs and is perfectly compatible with the USRP-2974 used in this system. The program developed by the authors for analyzing PDP and corresponding channel parameters based on collected data is shown in Figure 9.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Parameters** & **Detail Information** \\ \hline Frequency Band & 27–29 GHz \\ Duplex mode & TDD \\ Array size & 8 x 8 \\ Unit spacing & 0.5 \(\times\lambda\) \\ G/T & \(\geq\)\(-\)7 dB/K \\ Polarization mode & Vertical polarization \\ Sidelobe suppression & \(\geq\)15 dB @ normal-direction \\ Beam scanning range & \(\pm\)60\({}^{\circ}\) @ Horizontal\&Vertical direction \\ Beam switching speed & \(<\)3 \(\upmu\)s \\ Transceiver switching speed & \(<\)3 \(\upmu\)s \\ EIRP & 51 dBm \\ Gain dynamic range & \(\geq\)30 dB \\ Gain adjustment accuracy & 1 dB \\ \hline \hline \end{tabular}
\end{table}
Table 4: Parameters of the mmWave phased-array antenna mmPSA-TR64 MX.  
\begin{table}
\begin{tabular}{l l c} \hline \hline  & **Parameters** & **Detail Information** \\ \hline \multirow{4}{*}{Baseband} & ADC resolution & 14 bit \\  & DAC resolution & 16 bit \\  & Maximum I/Q sample rate & 200 MS/s \\  & ADC sFDR & 88 dB \\  & DAC sFDR & 80 dB \\ \hline \multirow{4}{*}{Transmitter} & Number of channels & 2 \\  & Frequency range & 10 MHz to 6 GHz \\  & Frequency step & \(<\)1 kHz \\  & Maximum output power & 5 mW to 100 mW \\  & Gain range & 0 dB to 31.5 dB \\  & Gain step & 0.5 dB \\  & Maximum instantaneous real-time bandwidth & 160 MHz \\ \hline \multirow{4}{*}{Receiver} & Number of channels & 2 \\  & Frequency range & 10 MHz to 6 GHz \\  & Frequency step & \(<\)1 kHz \\  & Gain range & 0 dB to 37.5 dB \\  & Gain step & 0.5 dB \\  & Maximum input power & 10 dBm \\  & Noise figure & 5 dB to 7 dB \\  & Maximum instantaneous real-time bandwidth & 160 MHz \\ \hline \hline \end{tabular}
\end{table}
Table 3: Parameters of USRP-2974.
## 8 Issues and Recommendations for Future Research
### Mobility and Context Awareness Issues  
In IoV, a thorough knowledge of vehicle mobility is a crucial aspect of continuous IoV services and network management. As vehicles move, the link condition between them and the RSU can change quickly and become intermittent. More significantly, an urban environment can be highly dynamic and complex with many unpredictable factors, such as time-varying traffic densities, buildings, and other structures affecting the V2V and V2I link performance. ICN was considered an alternative to bypass the limitations imposed by traditional IP networks, especially those relating to mobility. Even so, several research challenges remain unresolved, including frequent handovers that may degrade QoS and QoE. ICN supports consumer mobility natively. However, it becomes more complex if the producer also moves. A potential solution could be to locate mobile producers by flooding them with interest packets for path discovery. However, this would increase network traffic and the likelihood of network congestion. Vehicle contextual information, including position, speed, direction, acceleration, road traffic, road condition, and weather information can be effectively obtained from onboard sensors and shared among vehicles to design intelligent context-aware IoV protocols.