esa-sceva/satcom-chunk-collection huggingface dataset:
# Review on Free-Space Optical Communications for Delay and Disruption Tolerant Networks
## 2 Free-Space Optical Communications
### Applications  
Lasercom technologies can be used for LEO and geostationary orbit (GEO) satellites, and deep-space probes, between themselves and to ground stations. Inter-satellite links are used in the ESA EDRS system, already operational [8], and in the envisioned ESA HyDRON project [9]. In a longer term, applications will embrace also future networks of airborne or space-borne platforms, including Earth to Moon or interplanetary links.  
Optical space networks have to be managed as terrestrial telecommunication networks, but, in space systems, any problem is exacerbated by the inability to easily reach and fix malfunctioning hardware [5]. These characteristics have to be taken into consideration when designing a space network, as the system must have a higher degree of reliability than corresponding terrestrial networks.
# A Disposable Soft Magnetic Ribbon Impedance-Based Sensor for Corrosion Monitoring
## 2 Materials and Methods
### Tests and Analysis of Chemical Corrosion  
The effect of the HNO\({}_{3}\)'s strength on the impedance was accessed by its real-time measurement via the drop-casting of a certain volume (\(\sim\)5 \(\upmu\)L) of HNO\({}_{3}\) at different concentrations (0.9, 1.8, 3.8, 5.5, and 7.4 Molar). The acid concentrations were selected considering the thickness of the amorphous strip used (\(\sim\)25 \(\upmu\)m). In order to determine the optimal operating frequency range for the chemical sensor, we performed impedance measurements at different frequencies (0.2, 2, 5, and 7 MHz). Ten trials were conducted for each frequency and acid concentration, using different identical ribbons each time, and the average of the data was then used for analysis. From the data stated, graphs containing the change in impedance as a function of the acid concentration and change in impedance as a function of time could be obtained and analyzed.
## 3 Therapeutic Potential of Exosomes in Different Diseases
### Therapeutic Potential of Exosomes in Obesity  
In obese individuals, inflammation resulting from adipose tissue has been documented as the main factor in the progression of type 2 diabetes and insulin resistance [107]. Researchers have identified that ADSCs have key roles in metabolic diseases and inflammation resulting from obesity. ADSC-Exos were explained to affect diabetes and obesity. In white adipose tissue (WAT), ADSC-Exos were shown to dominate the anti-inflammatory (M2) macrophage phenotype polarization and regulation of immune homeostasis. In WAT obese mice, exosomes have been observed to promote polarization of the M2 macrophage and inflammation prevention [55; 108]. Of note, M2 macrophages resulted from ADSC-Exos exerting high levels of tyrosine hydroxylase potentiating the production of catecholamine, which promotes brown adipose tissue-specific uncoupling protein 1 expression in WAT which activates fat burning leading to energy reserve dissipation. This observation highlights the indispensable exosome-mediated crosstalk existing between macrophages and ADSCs and attracts attention to the potential role of an exosome-based treatment approach to treat obesity [109].
## 4 Statistical Testing of ENMRA Algorithm
### System Model  
#### 5.2.1 Modulation  
As observed in [55], fewer variations of the channel were faced by MI communication than EM waves. Advanced modulation techniques may result in more energy consumption with energy-restricted MI-USNs [44]. For underground communication, many easy and suitable modulation techniques can be picked up, e.g., BFSK, BPSK, QPSK, and 16-QAM. Given the connection \(i-j\), \(m_{ij}\in\mathcal{M}\) represents the adopted modulation technique and \(\eta(m_{ij})\) signifies the modulation spectrum efficiency. The bit error rate (BER) \(\psi\) can be obtained for the modulation scheme \(m_{ij}\) over link \(i-j\), called the received SNR, \(\psi_{ij}^{m_{ij}}\).
# Evaluation of Structural Behavior of Hysteretic Steel Dampers under Cyclic Loading
## 2 Experimental Study
### Test Setup and Loading Procedure  
As shown in Figure 2, four identical dampers were installed on each specimen to prevent eccentric deformation of the damper device and to distribute the force acting on the bolt fastened to the shear plates. The dampers were connected to the tops and bottoms of the fronts and ears of T-shaped loading plates, which were connected to L-shaped steel loading frames. As shown in Figure 2, two L-shaped loading frames were used in this experiment: one fastened to the bottom steel frame and the other to a loading actuator of a universal testing machine (UTM) with a capacity of 2000 kN. When a load was applied to the top of the L-shaped loading frame, the loading frame moved vertically, and the shear force was transmitted to the dampers via loading plates.  
To maintain a constant distance between the two loading frames, each roller with a capacity of 300 kN was installed on the upper and lower portions of the L-shaped steel loading frames. As shown in Figure 3, the repeated load with the target displacement of \(\pm\)15 mm was applied to the specimens at  
Figure 1: Details of steel dampers used in specimens (unit: mm): (a) SD1; (b) SD2; (c) SD3.  
Figure 2: Test setup of specimen.  
a speed of 0.06 mm/s to evaluate whether there were changes in the strength, stiffness, and energy dissipation capacity of the specimens during the test.  
To measure the relative displacement of the specimens, each LVDT (linear variable differential transducer) was installed on the front and rear surfaces of the loading plates. To check the yielding and deformation characteristics of the specimens, strain gauges were attached to the ends of vertical and horizontal members where the maximum moment occurred. In addition, a strain gauge was attached to the top of each semicircular member to evaluate the deformation characteristics of the connecting members of SD1 and SD3.
# Influence of Alternating Temperature on the Effectiveness of EICP in Consolidating Aeolian Sand
## 2 Materials and Methods
### Method
#### 2.2.4 Shear Wave Velocity Test  
In this regard, shear velocity wave values were measured by an ultrasonic detector (Proceq Pundit PL-200, Proceq Trading Shanghai Co. Ltd., Shanghai, China) with an input signal at 250 kHz frequency. To ensure that the samples were as unaffected by room temperature, each test was controlled to be completed within 15 min, and the steps were as follows: Fifteen sand column samples were removed from the temperature maintenance chamber every 12 h. The length of the sand column in each mold was measured and recorded as \(L_{i}\). The ultrasonic transducers were evenly covered with coupling and closely attached to both ends of the sand column. The transmission time of the shear waves through the waveform graph on the ultrasonic detector was obtained and recorded as \(t_{i}\).  
The length of the sand column (\(L_{i}\)) and transmission time (\(t_{i}\)) were accurate to 0.1, and the tests were repeated five times to take the average value. The shear wave velocity was calculated according to Equation (3).  
\[v=\frac{L_{i}}{t_{i}} \tag{3}\]
## 2 Material and Methods
### 3D Cephalometric Analysis  
Craniofacial computed tomography (CT) images were taken before (T1), immediately after TPD (T2), and 6-months post-T2 (T3) with the use of a 64-row scanner (GE Healthcare) at 0.625 mm slice thickness, with a pitch of 1.375. Tube voltage was set at 120 kV and tube current was selected individually for each patient. CraniCT images in DICOM (Digital Imaging and Communications in Medicine) format were imported into the Dolphin Imaging version 11.7 software (Chatsworth, CA, USA), which allowed construction of 3D models. One investigator identified the reference points then obtained linear and angular measurements (Figures 1 and 2). To determine the intraobserver reliability, 10 randomly selected computed tomography images were analyzed for a second time (with an interval of 7 days).
# Development of a Robotic Arm Based Hydrogel Additive Manufacturing System for In-Situ Printing
## 3 Forming Process
### Results
#### 3.3.4 Mechanical Testing  
The obtained elastic modulus had a linear proportion to the PEGDA concentration (as shown in Figure 6). The elastic modulus of 15% PEGDA was 0.266 \(\pm\) 0.006 MPa. In contrast, the elastic modulus of 35% PEGDA was 2.495 \(\pm\) 0.011 MPa. The elastic modulus of the 20% PEGDA we used was 0.770 \(\pm\) 0.006 MPa.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & **Value** & **Unit** \\ \hline Droplet diameter \(d\) & 1.3 & mm \\ Light spot diameter \(D\) & 6.0 & mm \\ Curing time \(t\) & 0.8 & s \\ Droplet spacing \(\Delta x\) & 0.75 & mm \\ Line spacing \(\Delta L\) & 0.75 & mm \\ Nozzle velocity \(v\) & 3.0 & mm/s \\ Nozzle frequency \(F\) & 4.0 & Hz \\ \hline \hline \end{tabular}
\end{table}
Table 2: Printing parameters of initial printing trials.  
Figure 5: (a) Square printing results; (b) circular printing results; (c) heart-shaped printing results. The printing path is in the upper left corner of each picture.  
Figure 6: The relationship between elastic modulus and poly(ethylene glycol) diacrylate (PEGDA) concentration. The data points were linked together by straight line. Error bars represent the standard deviation. Six samples were taken for each concentration.
## 5 Examples
### Appropriate Matching Location of Fixed Bearing with Rer
#### 5.2.1 Shorter Continuous Beam  
Since the critical length is 164 m, the lengths of 120, 135, and 150 m are studied as examples of the shorter length. RER above the middle of the continuous beam remains unchanged. Figure 10 shows the curves of the maximum additional tension and compression forces of rail with the change in location of the fixed bearing, in which the abscissa represents the distance of the fixed bearing and the left end of the continuous beam. It can be found from Figure 10 that the location of the fixed bearing has an obvious influence on the maximum additional forces of rail, and the maximum additional tension and compression forces of rail reach their minimum values when the abscissas are 44, 51.5, and 59 m, corresponding, respectively, to the lengths of 120, 135, and 150 m. At this moment, the fixed bearing is not located at the midpoint \(\mathrm  
extreme values of the additional tension force of rail, on the left and right sides of the RER on the continuous beam, is approximately equal, and they are also maximum tension forces corresponding to the \(l_{\text{fb}}\) of 59 m.  
The additional compression force of rail above the left end of the continuous beam in the curve of \"fb 84 with RER\" is almost equal to that above the right end of the continuous beam in the curve of \"fb 34 with RER\", and they are maximum compression forces corresponding to the \(l_{\text{fb}}\) of 84 and 34 m. At the same time, the extreme values of the additional tension force of rail on the left side of RER in the curve of \"fb 34 with RER\" is approximately equal to that on the right side of RER in the curve of \"fb 84 with RER\", and they are maximum tension forces corresponding to the \(l_{\text{fb}}\) of 34 and 84 m. This helps explain why the maximum additional forces in Figure 10 are symmetrical.  
Figure 12 draws the curves of corresponding longitudinal displacement of rail. Similarly, the longitudinal displacement of rail is discontinuous at the location where RER is set. Since the RER is located above the middle of the continuous beam, the displacement of rail at the two ends of RER varies with the location of the fixed bearing.
# Fault-Resilient Manufacturing Scheduling with Deep Learning and Constraint Solvers
## 2 Related Work
### Comparison with Existing Approaches
#### 2.3.1 SMT-Based Scheduling  
[PERSON] et al. [12] proposed a system that integrates SMT solvers with optimization techniques to address complex scheduling problems in multi-machine environments under various constraints. Their research underscores the role of SMT in enhancing decision-making within real-time scheduling scenarios. [PERSON] and [PERSON] [13] developed an SMT-based approach for static schedule generation in time-triggered networked systems, specifically targeting task- and network-level scheduling for time-sensitive, preemptive systems. Their approach employs an incremental scheduling method to improve scalability for complex systems. [PERSON] and [PERSON] [14] further enhanced SMT-based scheduling by integrating network calculus analysis to manage the coexistence of time-triggered (TT) and rate-constrained (RC) traffic in Ethernet networks. Unlike these studies, our work uniquely incorporates real-time fault prediction to dynamically adjust job assignments, making it more adaptable to dynamic edge computing environments.  
To complement SMT-based methods, researchers have also explored approaches that focus on anticipating machine failures. The following section reviews literature on machine learning techniques employed for predictive maintenance.
## 5 Conclusions and Outlook  
The work here describes a genetic engineering approach that enables higher curdlan yields. However, there is much work still needed to establish optimal conditions. Now that the high expression of the _crdASC_ genes is fixed, which sources of N and C are most suitable for high curdlan yields? What concentrations of P and dissolved oxygen are optimal, and at what pH? Such questions have been intensively addressed for _agrobacterium_ sp. 31749 [21; 27; 48], but these should be re-addressed for the genetically modified strains. I anticipate that the 96-well plate assay first described in this study could be a useful tool for answering these questions. Furthermore, which strains are most useful for high quality, high purity curdlan production? My approach using PphaP showed that curdlan production can be enhanced in both 31749 and C58 strains, despite the 1% genetic differences between the strains. Note that this difference likely accounts for different growth behavior (31749 reaches a much higher cell density in liquid cultures than C58, see Figure 3A) which could also account for the higher yield per liter of culture by strain 31749. However, strain C58 might be better at growth on various fermentation substrates, e.g., plant biomass.  
Various plant biomasses have been tested as sources of carbon for curdlan production, such as sugar cane, cassava starch, wheat bran, orange peels and prairie cordgrass [49; 69; 70; 71; 45]. To my knowledge, however, apple extract has not been tested for curdlan production. Apples made up about 80-90% of all tree fruits harvested in Germany ([[https://www.destatis.de/EN/Themes/Economic-Sectors-Enterprises/Agriculture-Forestry-Fisheries/Fruit-Vegetables-Horticulture/_node.html](https://www.destatis.de/EN/Themes/Economic-Sectors-Enterprises/Agriculture-Forestry-Fisheries/Fruit-Vegetables-Horticulture/_node.html)]([https://www.destatis.de/EN/Themes/Economic-Sectors-Enterprises/Agriculture-Forestry-Fisheries/Fruit-Vegetables-Horticulture/_node.html](https://www.destatis.de/EN/Themes/Economic-Sectors-Enterprises/Agriculture-Forestry-Fisheries/Fruit-Vegetables-Horticulture/_node.html)) (accessed on 7 August 2023)) and is therefore a local, cheap carbon source for curdlan production. In particular, the waste associated with apple storage, transport and pulp from juicing might be used for curdlan production. I found that apple extract added to agar at 10% supported what appeared to be a high yield of curdlan. It is even possible that apple extract might be superior to glucose as a carbon source, although this needs further research. Thus, apples could be cheap source of carbon for curdlan production. Combined with the new strains developed in this study that are not sensitive to nitrogen levels, new fermentation processes based on a variety of cheap plant biomass sources could dramatically decrease the cost of curdlan production, extending the use of curdlan in industry.
# Matching-Based Resource Allocation for Satellite-Ground Network
## 3 System Model
### Delay Model  
Since users are mainly located in remote areas, the connection between users and LEO satellites is obtained. The communication channel between the user and the satellite is mainly related to the distance from the user to the LEO satellite. According to [15], the received power of satellite \(l_{j}^{t}\) at slot \(t\) is:  
\[P_{j,t}^{re}=\frac{P_{i}^{tr}G_{i}^{tr}G_{j}^{re}c}{4\pi fH_{i,j}^{t}} \tag{1}\]  
where \(G_{i}^{tr}\) represents the user's transmit antenna gain, and \(G_{j}^{re}\) represents the satellite's receive antenna gain. \(H_{i,j}^{t}\) is the distance from the LEO satellite to the user at slot \(t\). \(c\) and \(f\) represent the speed of light and the carrier frequency, respectively. The achievable rate from user to satellite is given by:  
\[R_{i,j,t}^{u\to s}=B_{i,j}^{t}log\left(1+\frac{P_{j,t}^{re}}{k_{B}T_{s}B_{i,j} ^{t}}\right) \tag{2}\]  
where \(k_{B}\) is the Boltzmann constant and \(T_{s}\) is the system noise temperature.  
Assuming that all tasks are transferred to satellites via U2L channels, the transmission delay experienced by users in relation to LEO satellites may be calculated by dividing the task data size \(\alpha_{i}^{t}\) by the associated transmission data rate \(R_{i,j,t}^{u\to s}\)  
\[T_{i,j,t}^{ul}=\frac{\alpha_{i}^{t}}{R_{i,j,t}^{u\to s}} \tag{3}\]
# Malware Collusion Attack against SVM: Issues and Countermeasures
## 5 Evaluation
### Q1&Q3: Effectiveness and Performance of ColluDroid
#### 5.1.1 Effectiveness of ColluDroid  
At first, we made four malicious apps manually. Then, we trained ColluDroid-Linear SVM and ColluDroid-Sec-SVM with our man-made apps and also other 5000 benign and 5000 malicious apps. The trade-off parameter \(C\) of Linear SVM and Sec-SVM are both set as \(C=1\). As for the bounds of Sec-SVM, they are selected in a set of options \(w^{lb},w^{ub}\in\{0.05,0.1,0.15,0.2,0.25\}\) with a 5-fold cross validation according to the Detection Rate. After selection, we set each element of \(w^{ub}\) and \(w^{lb}\) as 0.15.  
Next, we split each man-made app into two apps. These split apps together with 200 other benign apps will be tested in the detection phase. Our methods need to find out which app set can perform Collusion Attack. Our man-made malicious payloads of each app and split apps are shown in Table 1.  
In the detection phase, we find 267,419 ICC links within the test apps, which means that there are 267,419 possible combinations among all the components. Then, we tested all the possible combinations with the Malware Detector. Both ColluDroid-Linear SVM and ColluDroid-Sec-SVM can successfully screen out the manually split eight apps and point out the components that result in the Collusion Attack.  
We tested the same data set on Hindroid and Drebin: both are excellent machine learning method based malware detection methods. As they are both designed to test a single app, this collusion attack cannot be addressed. Our experiment demonstrated that it is possible to split an existing malicious app into two apps, and evade the detection of the current machine learning method. However, when we apply our method to 2000 real world apps that we crawled from Google Play, unfortunately, we do not find any collusion instance. Maybe it is because the number of apps in our test set is too small that there isn't any app that has the tendency to make such a attack.
## 3 Results and Discussion
### Corrosivity and Water Content  
Figure 16 shows the effects of the cement content, initial water content, and curing time on the water content and pH value. As is seen in Figure 16a, there was a slight decrease in the water content with the increasing cement content, attributed to the consumption during the hydration reaction of cement. Notably, the water content increased significantly with the higher initial water content, as shown in Figure 16b. The addition of PCE had a minimal impact on the water content. The incorporation of cement and PCE initially led to a significant reduction in the water content of the soil-based CLSM. As the curing period advanced, the water content continued to decrease gradually. This trend aligns with the observed variation in the water content for cement-stabilized dredged soil, indicating an initial rapid decrease followed by a gradual decline with the increasing curing time, according to [PERSON], et al. [56].  
In Figure 16, the pH value exhibited a noticeable rise with the increasing cement content and a slight decrease with the higher initial water content. The addition of PCE had little effect on the pH value. The higher cement content contributed to increased hydration products, particularly calcium hydroxide, leading to an overall elevation in the pH value. A pH below 2.5 or above 12.5 indicates corrosiveness [57]. Consequently, the soil-based CLSM was non-corrosive and posed no harm to the environment when the cement content did not exceed 20%. It is an environmentally friendly and sustainable construction material.
## 6 Discussion
### Limitations and Future Research  
The conclusions of this study should be interpreted within the context of several important limitations. The experimental investigation was conducted on a single concrete grade using specimens with a single, centrally located reinforcement bar, resulting in a relatively low reinforcement ratio (approx. 0.66%). While this controlled approach was necessary for establishing fundamental principles, it also defines the boundary conditions for our \"matrix-dominated\" conclusion. At significantly higher reinforcement ratios, for instance, the volume of concrete influenced by the multi-axial stress fields near the steel-concrete interfaces would increase. This could potentially challenge the validity of the one-dimensional stress assumption across a larger portion of the specimen, possibly leading to a scenario where the reinforcement configuration itself begins to influence the measured spall strength. Similarly, different distributions (e.g., multiple, smaller-diameter bars) or larger rebar diameters could introduce more complex wave scattering phenomena or alter bulk wave propagation. Future research should therefore systematically explore this broader parameter space to comprehensively map the boundary between matrix-dominated and reinforcement-influenced dynamic tensile behavior.  
The strain rate regime examined in this study spans an intermediate range (approximately 3 s\({}^{-1}\) to 55 s\({}^{-1}\)), which is representative of many practical loading scenarios. However, extending investigations to higher strain rates (>100 s\({}^{-1}\)) would provide valuable insights into UDRC behavior under more extreme loading conditions, such as those encountered in blast or impact scenarios. Additionally, advanced multiscale modeling approaches that incorporate detailed steel-concrete interface behavior could offer deeper understanding of local damage initiation and progression mechanisms.  
Another significant limitation is that all specimens were tested under controlled uniform moisture conditions to isolate the effects of reinforcement from environmental factors. While this approach was essential for validating the fundamental applicability of the Novikov formula to UDRC, it does not reflect the heterogeneous moisture content typical of real operating conditions. Future research should investigate how moisture gradients--common in actual structures due to environmental exposure, differential curing, and seasonal variations--affect UDRC dynamic behavior. This work should include developing correction methodologies and establishing testing protocols that account for realistic service environments, thereby bridging the gap between controlled laboratory validation and practical field applications.