esa-sceva/satcom-chunk-collection huggingface dataset:
# The Traffic Adaptive Data Dissemination (TrAD) Protocol for both Urban and Highway Scenarios
## 4 Performance Evaluation
### Different Traffic Routes
Furthermore, TrAD also uses a SCF-agent rebroadcast constraint technique to further suppress transmissions. For UV-CAST, the gift-wrapping algorithm triggers more transmissions, which also brings the extra delay. For AMD, although it could recognize the relatively regular Manhattan map, there is a drawback in its SCF mechanism. The SCF mechanism only triggers the transmission of stored data while the transition, _i.e._, from tail to non-tail, happens, but, due to signal fading or interference, the stored data is not always received, especially in the scenarios with deviated traffic routes. In case a transmission fails, there is not another chance to trigger the SCF-agent to forward the stored data again.
## 3 Simulation Environment and Approaches
### Software and Hardware Configuration for PIL and HIL  
#### 3.3.1 Characteristics of the PIL and HIL Environment  
Processor-in-the-Loop (PIL) simulations are employed to incorporate sensor simulation feedback and emulate the space environment, enabling the verification of ADCS behavior as though operating in space. The PIL method establishes a foundational framework for the pilot phase of the system including the target processor. In our configuration, Matlab in the host computer connects to the STM32 via a serial connection. During a representative Simulink simulation, the plant model transmits data to the controller in the form of a 32-byte vector, with each element being an 8-bit integer. Upon receiving the data, the controller processes the information for one sample interval by executing its C-generated code, and then sends the output back to the Simulink real-time simulation, completing one sample cycle. Additionally, the HIL real-time simulations incorporate specific hardware components to increase the fidelity of the simulation. In particular, a Helmholtz cage is utilized to derive magnetic field values, simulating the geomagnetic field experienced by the CubeSat in its body frame \(\mathcal{B}\) during its orbital motion. The difference between the PIL and HIL approaches lies in the real magnetometer measurement for HIL. Nevertheless, PIL employs the magnetometer data simulated by the space environment. Finally, it is emphasized that both PIL and HIL consider the space environment developed for MIL. So, the Simscape model developed for sun sensors and magnetorquers is simulated during the real-time tests.  
Figure 7: Software-in-the-Loop environment diagram; this diagram includes the process for C code generation for SIL, PIL, and HIL.
###### Abstract  
To improve the accuracy of winter wheat yield estimation, the Crop Environment Resource Synthesis for Wheat (CERES-Wheat) model with an assimilation strategy was performed by assimilating measured or remotely-sensed leaf area index (LAI) values. The performances of the crop model for two different assimilation methods were compared by employing particle filters (PF) and the proper orthogonal decomposition-based ensemble four-dimensional variational (POD4 DVar) strategies. The uncertainties of wheat yield estimates due to different assimilation temporal scales (phenological stages and temporal frequencies) and spatial scale were also analyzed. The results showed that, compared with the crop model without assimilation and with PF-based assimilation, a better yield estimate performance resulted when the POD4 DVar-based strategy was used at the field scale. When using this strategy, root mean square errors (RMSE) of 523 kg\(\cdot\)ha\({}^{-1}\), 543 kg\(\cdot\)ha\({}^{-1}\) and 172 kg\(\cdot\)ha\({}^{-1}\) and relative errors (RE) of 5.65%, 5.91% and 7.77% were obtained at the field plot scale, a pixel scale of 1 km and the county scale, respectively. Although the best yield estimates were obtained when all of the observed LAls were assimilated into the crop model, an acceptable estimate of crop yield could also be achieved by assimilating fewer observations between jointing and antisis periods of the crop growth season. With decreasing assimilation frequency and pixel resolution, the accuracy of the crop yield estimates decreased; however, the computation time decreased. Thus, it is important to consider reasonable spatio-temporal scales to obtain tradeoffs between accuracy and effectiveness in regional wheat estimates.  
assimilation; crop model; leaf area index; particle filter; four-dimensional variational; uncertainties; spatio-temporal 2017  
10.3390/rs9030190
## 2 Circuit Topology of RF-Energy-Harvesting Integrated Circuit
### Charge Control Circuit  
Figure 8 presents the charge control circuit, which was used to control the charge in the battery by using the supplied output voltage V\({}_{\text{dd}}\) of the LDO regulator. The control circuit comprised a differential pair (Q\({}_{1}\)-Q\({}_{3}\)), current mirror (Q\({}_{4}\)-Q\({}_{5}\)), and comparator (Comp) to prevent the overcharging of the battery. Channel length modulation and body effect were assumed negligible, and the differential pair operated in a saturation region. The battery was charged using a constant current, which was generated using Q\({}_{1}\) and Q\({}_{2}\) with two bias voltages (V\({}_{\text{B1}}\) and V\({}_{\text{B2}}\)). When the battery voltage V\({}_{\text{bat}}\) was lower than the reference voltage V\({}_{\text{ref}}\), the output voltage of the comparator increased to a high level (1) and Q\({}_{3}\) was turned off. Two constant currents of Q\({}_{1}\) and Q\({}_{4}\) simultaneously flowed into Q\({}_{2}\). The conduction current of Q\({}_{5}\) was amplified N times with (W/L)\({}_{5}\) = N\(\times\)(W/L)\({}_{4}\) when it was passed through the current mirror Q\({}_{4}\)-Q\({}_{5}\). This large current rapidly charged the battery. If V\({}_{\text{bat}}\) was higher than V\({}_{\text{ref}}\), the output voltage of the comparator was low (0) and Q\({}_{3}\) was turned off. Two constant currents of Q\({}_{1}\) and Q\({}_{3}\) simultaneously flowed into Q\({}_{2}\). Moreover, Q\({}_{4}\) and Q\({}_{5}\) were turned off, and the battery was not further charged.
# Security of IoT Application Layer Protocols: Challenges and Findings
## 6 Discussion
Our analysis is based on CVSS version 2 since the scores for the latest CVSS version 3.1 were unavailable for some of the analyzed CVEs. It is also important to outline that security risks and vulnerabilities expose IoT devices to a wide range of threats and attacks (see Table 5) that could have very serious effects. We notice that constrained devices are especially vulnerable to DoS and DDoS attacks mainly because of their limited capabilities or of an incorrect configuration. Attackers can easily cause temporary or permanent failures of a service by flooding a device with connection attempts that drain its battery or by performing amplification/reflection attacks that simply exploit device vulnerabilities. It is also important to outline that the UDP transport protocol is the main attack vector for application layer protocols, such as CoAP, mDSN and SSDP. Good practices and measures aimed at mitigating the security risks and reducing the attack surface have been proposed by several papers. Table 6 presents the breakdown of the papers appeared in the literature as a function of the protocol and security service. We notice that most works focused on MQTT and CoAP protocols and in particular on the development of lightweight encryption mechanisms able to cope with the constrained characteristics of IoT devices. On the contrary, despite the serious security risks affecting service discovery protocols, little research efforts have been dedicated to mitigate the potential attacks. We also outline that our \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Protocol**} & \multicolumn{3}{c}{**Severity**} & \multirow{2}{*}{**CVSS2 Score**} \\ \cline{2-2} \cline{4-5}  & **Low** & & **Medium** & **High** \\ \hline MQTT & 3 & 42 & 12 & 5.6 \\ \hline CoAP & 0 & 5 & 2 & 6.6 \\ \hline AMQP & 11 & 50 & 17 & 5.2 \\ \hline DDS & 0 & 5 & 0 & 5.0 \\ \hline XMPP & 5 & 70 & 19 & 5.6 \\ \hline mDNS & 0 & 16 & 13 & 6.4 \\ \hline SSDP & 5 & 49 & 27 & 5.9 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Per protocol breakdown of the number of CVEs according to their severity and overall CVSS2 score. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Protocol**} & **Eavesdropping Attacks** & **IP Spoofing Attacks** & **DoS/DDoS** & **MiTM Attacks** & **Poisoning Attacks** \\ \hline MQTT & & & \(\bullet\) & \(\bullet\) & \\ \hline CoAP & & \(\bullet\) & \(\bullet\) & \\ \hline AMQP & & & \(\bullet\) & \\ \hline DDS & & & \(\bullet\) & \\ \hline XMPP & & & \(\bullet\) & \\ \hline mDNS & \(\bullet\) & \(\bullet\) & \(\bullet\) & \(\bullet\) \\ \hline SSDP & \(\bullet\) & \(\bullet\) & \(\bullet\) & \(\bullet\) & \(\bullet\) \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Summary of the major attacks affecting the application layer protocols analyzed in this paper. search did not produce any relevant paper proposing mitigation measures for the AMQP, DDS and XMPP protocols.
## 3 Test Results and Analysis
### Analysis of Damage Patterns  
The stress-strain curves of the intact specimen, ultra-fine cement grouting specimen, C-S grouting specimen, and MARITHAN(r) grouting specimen are shown in Figure 7. It can be seen that the stress-strain curves of the specimens after grouting reinforcement are similar to the curves before grouting, and they all experience a peak from the elastic phase to the destructive phase. The intact specimen shows ductile damage characteristics during uniaxial loading, and the stress-strain curves do not show obvious peaks; the grouted specimens show brittle damage characteristics during uniaxial loading, and the stress-strain curves decrease more rapidly when reaching their peak strength.  
Figure 6: Comparison of grouting effects.  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Grouting Material** & **Specimen** & **Uniaxial** & **Average Uniaxial** & **Peak Strength** & **Residual Strength** \\
**Number** & **Compressive** & **Compressive** & **Enhancement** & **Enhancement** \\
**Strength** & **Strength** & **Strength** & **Coefficient** & **Coefficient** \\ \hline \multirow{3}{*}{MARITHAN®} & C-1 & 24.753 MPa & & & \\  & C-2 & 29.453 MPa & 26.468 MPa & \(-1.923\)\% & 39.998\% \\ \cline{1-1}  & C-3 & 25.197 MPa & & & \\ \hline \end{tabular}
\end{table}
Table 3: _Cont._To investigate the cause, the damage characteristics of the specimens after uniaxial compression were observed, as shown in Figures 8-10. The damage pattern of the ultra-fine cement grouting specimen shows shear-slip damage along the original rupture surface, and the specimen has a few secondary cracks. The damage pattern of the C-S grouting specimen shows larger shear-slip damage along the original rupture surface, and part of the grouting specimen is crushed and peeled off, indicating the lower shear and tensile strengths of the fissure bonding weak surface of the grouting specimen. For the MARTHAN(r) grouting specimen, the whole loading process is basically the same as the intact specimen, and there is no obvious cracking during the loading process; when the load reaches its peak, the specimen cracks from a position near the end of the specimen without damage to the grouted body portion.  
Figure 8: Damage pattern of intact specimens. (**a**) Before injection of ultra-fine cement slurry; (**b**) Before injection of C-S slurry; (**c**) Before injection of MARTHAN®.  
Figure 7: Stress–strain curves of specimens under uniaxial compressive load.  
Figure 9: Damage pattern of grouting specimens. (**a**) After injection of ultra-fine cement slurry; (**b**) After injection of C-S slurry; (**c**) After injection of MARTHAN®.  
From the perspective of strength reinforcement with grouting, the ultra-fine cement slurry and C-S slurry do not exhibit a good reinforcing effect due to their low adhesive properties and permeability. However, MARITHAN(r) can effectively improve the stress concentration caused by cracks, weaken the impact of prefabricated cracks on the macro-damage of the specimen, and exhibit a better reinforcing effect.
# Using Deep-Learned Vector Representations for Page Stream Segmentation by Agglomerative Clustering
## 4 Results and Discussion
### RQ1: How Much Does Supervision Help Clustering?  
By using finetuned embeddings from the VGG16 binary classification model trained on the dataset, some of the label information is retained in the embeddings. We expect this to be reflected in the vectors, and as a result we expect the vectors are tuned towards more specific regions of the pages.  
With the use of GradCam [23], we found that the finetuned vectors from the classification model focus primarily on email headers or contact information in letters, as visible in Figure 2a,b. For the pretrained vectors, this is not the case, and the pretrained representations are based on more general features such as the page structure or whether or not a page contains an image or a table, as seen in Figure 2c,d. We believe that these pretrained embeddings yield a more intuitive separation of pages. For example, with pretrained representations, the distance is high between a page with only an image and a page with only text. The finetuned embeddings from the classification model on the other hand see both these pages as \"uninteresting\" (classified as 0), which causes the distance to be low. However, as can be seen in Table 1, this intuitive way of separating pages is too simplistic for a complex task such as PSS. We calculated the average distance between pages from the same documents and with pages from different documents and found that there was almost no difference, with an average cosine distance of 0.232 and 0.236, respectively. Therefore, we have to conclude that our hypothesis was wrong and that pages from the same document are not more similar to each other than pages from different documents. In fact, documents in our dataset do not have a big variety and start pages can be identified by considering only a few features. We suspect that with more variety in documents, clustering might perform better. Simultaneously, if there are more types of start pages, classification would likely perform worse.  
\begin{table}
\begin{tabular}{l c c} \hline \hline
**Method** & **Page F1** & **Panoptic Quality** \\ \hline \multicolumn{3}{c}{Baseline} \\ \hline Mean Document Length & & \\ Baseline & 0.28 & 0.14 \\ \hline \multicolumn{3}{c}{Classification Approach} \\ \hline VGG16 & 0.86 & 0.68 \\ VGG16 (K-given) & 0.92 & 0.87 \\ \hline \multicolumn{3}{c}{Clustering Approach} \\ \hline Clustering with finetuned & 0.54 & 0.25 \\ embeddings & 0.61 & 0.52 \\ Clustering with pretrained & 0.49 & 0.24 \\ Embeddings & 0.43 & 0.29 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Average F1 scores over 33 streams (6.476 docs with 25.124 pages) for the classification and clustering methods as well as the mean document length baseline. For the clustering methods, we assume K to be known.
## 2 Optical Inspection Technologies in Railways
### Sensor Systems
#### 2.3.1. Sleepers  
According to worldwide studies, the main causes of concrete sleeper failure are rail seat deterioration and installation and tamping damage. The top five causes of failure are tamping damage, cracking from center binding, cracking from environmental or chemical degradation, cracking due to dynamic loads, and wear or fatigue of the shoulder/fastening system. [15] The main types of cracks of concrete sleepers are given in Figure 4[50].  
Visual inspection is still the most common assessment method for railway sleepers [51]. New developments are moving toward so-called smart sleepers. Here, measurement sensors are built into or onto the sleeper. The limitations of using smart sleepers include their production costs and the limited data on long-term performance, which can be incompatible with the lifetime of the sleeper. An overview is given in [52]. bvSys have developed a camera system for checking for cracks in concrete sleepers or roadways [20]. Four line-scan cameras, mounted underneath the vehicle, record the sleepers at speeds of up to 100 km/h with the aid of LED lighting. Cracks with a width of 0.5 mm or more are detected automatically. The manufacturer specifies a resolution of 0.5 \(\times\) 0.5 mm. Other systems are based on recording the entire railway track. A distinction can be made between the inspection of the track geometry and the inspection of rail defects [53]. The determination  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  
of track geometry features includes the track gauge, cross slope/cant, longitudinal plane, alignment, and twist [54]. The V-Cube measuring system from MERMEC is designed to detect 50 different types of defects in sleepers, ballast, and fastenings and to perform rolling surface analysis [21]. The data are captured by means of images that are analyzed using algorithms and a model-based approach.  
A disadvantage of the purely optical examination of the sleepers lies in the limitation of the visible areas. Only the area that is not covered by the ballast, track, or fastenings can be recorded. No statements can be made about the depth of the cracks, nor whether there are changes over time as there are usually no comparisons over several measurements due to the lack of localization.
# Application of Artificial Neural Networks in Assessing Mining Subsidence Risk
## 5 Testing the Model Using New Data  
The applicability of the subsidence risk grade was tested with newly collected 22 subsidence data from five abandoned mines. The data consist of 12 locations (6 subsidence and 6 no subsidence locations) at 3 coal mines and 10 locations (7 subsidence and 3 no subsidence locations) at 2 other types of mines. The information of data is summarized in Table 4.  
Figure 17: Subsidence risk grade for coal mines and other types of mines: (**a**) training data and (**b**) validation data.  
Figure 18: Summary of the subsidence risk grade analysis procedure.  
The subsidence risk grade analyses were carried out through the procedure illustrated in Figure 19. The results showed that five of six coal mine locations where subsidence had occurred were predicted to be L2 and that six of the seven locations for other types of mines were predicted as L2. For both types of mines, no location was predicted to be L1. Finally, it was found that the correct prediction percentage was 83% (5/6 subsidence locations) for coal mines and 86% (6/7 subsidence locations) for the other types of mines.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Type**} & **Mine** & **No.** & **Subsidence** & \begin{tabular}{c} **Elapsed** \\ **Time 1** \\ **Year** \\ \end{tabular} & **RMR** & \begin{tabular}{c} **Groundwater** \\ **Level (m)** \\ \end{tabular} & **Outcrop** & \begin{tabular}{c} **Load** \\ **Type 2** \\ \end{tabular} & \begin{tabular}{c} **Depth 3** \\ **(m)** \\ \end{tabular} & \
\begin{tabular}{c} **Height 4** \\ **(m)** \\ \end{tabular} &
\begin{tabular}{c} **Distance 5** \\ **(m)** \\ \end{tabular} \\ \hline \multirow{8}{*}{Coal} & 1 & Yes & 15 & 25 & 18 & Yes & 0 & 3 & 16 & 0 \\ \cline{2-11}  & 2 & Yes & 15 & 20 & 13 & Yes & 1  
It is thought that the main reason for the lower correct prediction percentage for the new data than the data for training was that the number of newly investigated locations was much smaller than the that for the training data.  
Finally, the results imply that the subsidence risk grade model could generally be used to assess the subsidence risk for an abandoned mine. Nevertheless, much more subsidence data with greater variations in ground conditions will be essential and various types of analyses by numerical and empirical approaches, etc. need to be combined to improve the correct prediction percentage of subsidence.  
Meanwhile, this analysis process can be used as an initial method screening the specific locations that are at high risk of subsidence, for subsequent detailed analyses by other approaches, for example, the amplitude or rate of the subsidence at high risk can be calculated by empirical methods, methods employing influence functions, and/or methods employing theoretical models [32].
# Finite Element Model of Soliosis Brace with Increased Utility Characteristics
## 2 Materials and Methods
### Modeling Using Finite Element Method  
In the current research, the parameters for a brace model were established and a computational model based on the finite element method (FEM) was developed by generating a finite element (FE) mesh. To match the conditions of a previous study [3], this model was configured to replicate the experimental tests, using the same triad of forces through supports \(R_{1}\) and \(R_{2}\), along with a preload force, \(F_{1}\). Two fixed supports were established, and an initial load of 36 N was applied, which is within the range of forces observed in actual braces. This preload was essential to stabilize the position of the brace on the test stand used in previous research [3]. In addition, the gravitational force, \(G\), was included in the model for a more complete representation [10].  
An additional \(F_{2}\) force of 0.1 N was then introduced to match the conditions of experimental tests using an Electronic speckle pattern interferometer (ESPI) system [3]. This optical interferometry system measured displacements along the Z axis of the computer model. The introduction of this smaller force was strategically planned to allow the measurement of small displacements within the interferometer's range while maintaining the linear elastic range of the material. The final simulation output, which calculated the shell displacement differences before and after the introduction of force F\({}_{2}\), was then processed for further investigation.  
Polypropylene was selected as the shell material for the orthosis because of its known durability and flexibility. It has a Young's modulus (\(E\)) of 1500 MPa and a Poisson's ratio (\(v\)) of 0.3, making it well suited to achieving the desired balance between stiffness and flexibility in an orthosis [11; 12].  
Figure 1: Diagram of the Boston brace for the left lumbar and a right thoracic curve pattern with forces applied.  
A 10-node tetrahedral element was used to construct the FEM model. This element type is particularly advantageous for accurately representing complex 3D geometries and provides excellent numerical performance for simulating the elastic behavior of materials such as polypropylene.  
An iterative process of mesh refinement was performed until the von Mises stress variation was less than 5% with increasing node density [3]. The final mesh contained 470,978 nodes and 265,348 finite elements with a maximum edge size of 8 mm.  
In addition to determining the distribution of von Mises stresses, the study also identified the field of principal stresses in the orthotic shell, represented graphically by a system of vectors for each shell element. The dense array of principal stress vectors allowed for the subsequent plotting of principal stress trajectories in key areas of the orthotic shell. These trajectories were then used to guide structural modifications aimed at improving the functional properties of the orthosis.