esa-sceva/satcom-chunk-collection huggingface dataset:
# Quadrupedal Robots' Gaits Identification via Contact Forces Optimization
## 3 Optimization Model
The related pseudocode is shown below:  
```
1:\(t\gets 0\)
2:Initialization parameters \(\left[T,V,m,I_{x},I_{y},I_{z},L,\ h,m_{p}\right]\)
3:GA random population initialization of \(\mathbf{p}_{GA}\) vector
4:While [GA's stop criterion is not met]
5:Vertical ground force definition with continuity kinematic constraints (periodicity condition)
6:Longitudinal and lateral forces with continuity kinematic and static friction cone constraints
7:Torques with continuity kinematic constraints in the linear case (small angles)
8:Initialization guess parameters for Nonlinear Programming
9:Evaluate Objective function for Nonlinear Programming
10:While {Nonlinear Programming's stop criterion is not met and the constraints are satisfied}
11:Sequential Quadratic Algorithm
12:Evaluate Objective function
13:Endwhile
14:Evaluate fitness function for each individuals
15:Select of the individuals
16:Recomhine of the individuals
17:Mutate of the individuals
18:Generations of new population
19:\(t\gets t+1\)
20:Endwhile
```  
**Algorithm 1**Pseudo code for the gait optimization algorithm. Figure 7: Optimization diagram of the genetic algorithm (GA) algorithm. Eventually, we introduce the multi-objective function to be minimized for the most efficient gait. The GA's fitness function is defined through the total specific energy \(E_{d}\), that is, the energy per traveled distance \(x(T)-x_{0}\) over the period, and the ground reaction jerk \(J_{GRF}\). The specific energy \(E_{d}\) includes the mechanical energy of the body and the swinging leg energy:  
\[ \begin{split} E_{d}=\frac{1}{x(T)-x_{0}}&\int_{0}^{T }\left|F_{x}\;\dot{x}\right|+\left|F_{y}\;\dot{y}\right|+\left|F_{z}\;\dot{z} \right|+\left|M_{x}\;\dot{\phi}\right|+\left|M_{y}\;\dot{\theta}\right|+\left| mg\;\dot{z}\right|\\ &+\left|\left(\sum_{i}m_{p_{i}}(1-w_{i}(t))\ddot{x}_{sw_{i}} \right)\dot{x}_{sw_{i}}\right|\;dt\end{split}  \tag{30}\]  
One can expect power fluctuations along the period with positive and negative values, perfectly balanced since these fluctuations have zero average (the system is conservative). Since \(E_{d}\) integrates the absolute value of the power, it measures the intensity of the power fluctuations. Requiring a small value for \(E_{d}\) means asking for small fluctuations of the power, which implies a benefit in terms of the actuator dimensions and power and thus of energy consumption. On the other hand, the request of a small jerk \(J_{GRF}\) assures traditionally smooth contact forces:  
\[J_{GRF}=\frac{1}{T}\int_{0}^{T}\parallel\dot{\textbf{F}}\parallel\;dt \tag{31}\]  
where \(\textbf{F}=\left[F_{x};F_{y};F_{z}\right]\). Multi-objective optimization is used to determine the Pareto frontier, the set of solutions that provides the optimal trade-off between the two criteria.
# Magnetic Fields Trump Oxygen in Controlling the Death of Erythro-Leukemia Cells
## 3 Results
### Necrosis  
All single Transitions, rise or fall, oxygen or MF, enhanced necrotic cell death (values in the first line of Table 4 larger than 1). But oxygen rise (hyperoxic) Transitions were more powerful (4.2, 3.9) than hypoxic ones (1.62, 1.3). MF rises at (3.9, 3.7) and MF falls (3.1, 2.7) rested between these extremes. MF changes only (\((2.7+3.1+3.9+3.7)/4=3.35\)) were overall more powerful than oxygen changes only (\((1.3+4.2+1.62+3.9)/4=2.76\)) in inducing necrosis.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c} \hline \hline
**Oxygen** & **Rise** & **Fall** & **Rise** & **Rise** & **H\%** & **Rise** & **L\%** & **Fall** & **L\%** & **H\%** & **Fall** & **Fall** & \\ \hline
**MF** & **Fall** & **Rise** & **Incu** & **Rise** & **Rise** & **Zero** & **Rise** & **Fall** & **Fall** & **Fall** & **Zero** & **Incu** & \\ \hline Necrosis & 4.46 & 4.21 & 4.2 & 4.1 & 3.9 & 3.9 & 3.7 & 3.5 & 3.1 & 2.7 & 1.62 & 1.3 & 3.4 \\ Late Apoptosis & 1.85 & 2.9 & 0.9 & 2.2 & 1.06 & 0.7 & 1.02 & 2.7 & 1.4 & 1.85 & 3 & 2.4 & 4.3 \\ Early Apoptosis & 2.5 & 2.3 & 1.2 & 1.65 & 1.33 & 0.8 & 1.28 & 2.6 & 1.5 & 0.3 & 0.3 & 0.3 & 0.3 & 8.7 \\ All Apoptosis & 4.35 & 5.2 & 2.1 & 3.85 & 2.39 & 1.5 & 2.3 & 5.3 & 2.9 & 2.15 & 3.3 & 2.71 & 3.5 \\ Late/Early Apoptosis & 0.74 & 1.26 & 0.75 & 1.33 & 0.80 & 0.88 & 0.80 & 1.04 & 0.93 & 6.17 & 10.00 & 7.74 & 13.5 \\ Total Damage & 8.81 & 9.41 & 6.3 & 7.95 & 6.29 & 5.4 & 6 & 8.8 & 6 & 4.85 & 4.92 & 4.01 & 2.4 \\ Necrosis/Apoptosis & 1.03 & 0.81 & 2 & 1.06 & 1.63 & 2.6 & 1.61 & 0.66 & 1.07 & 1.26 & 0.49 & 0.48 & 5.4 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Cell death analysis for all Transitions, relative to baselines. Oxygen “rise” is L% to H%. “Fall” is H% to L%. MF “rise” is Zero to Incubator Field. “Fall” is Incubator to Zero Field. “H%”, “L%”, “Zero”, and “Incu” refer to unchanged conditions. Total damage = necrosis + late apoptosis + early apoptosis. “\(\Delta\)” is the ratio of maximum to minimum on a line.  
Figure 3: Relative amounts of necrosis, early, and late apoptosis associated with oxygen falls/rises and magnetic field (MF) falls/rises.  
Table 4 also shows that overall, double Transitions (MF plus oxygen changes) generated more cell death than single MFs or oxygen Transitions (((4.46 + 4.21 + 4.1 + 3.5)/4)/(3.35 + 2.76)/2) = 1.33).  
The largest amplification of necrosis (4.46) occurred in the double Transition of Incubator to Zero MF (MF fall), and Low to High oxygen (oxygen rise).
# Nanotechnology Solutions for Controlled Cytokine Delivery: An Applied Perspective
## 3 Nanoparticles as Cytokine Delivery Systems  
The use of cytokines for disease treatments has a large range of applications by modulating the immune system. However, systemic administration generally results in harmful side effects due to the toxicity associated with high concentrations of cytokines required to achieve a therapeutic outcome [30]. Nowadays, there is a wide choice of possible nanomaterials to be used to reduce these and other side effects, though not all vehicles may be suitable for the delivery of cytokines. Solvent, pH, temperature, charge, size and other parameters used in the nanoformulations will influence the encapsulation efficiency, nanomaterial toxicity or even cytokine stability [31]. The latter is one of the major reasons why the delivery of cytokines is yet to be fully explored. Regardless, nanoparticles remain the most used vehicle for this purpose and many types have been studied that include sources such as polymeric [32; 33], chitosan [14], gold [34], silica [35], magnetic [36] to liposomal [37; 38] nanoparticles (Table 1).  
Figure 1: Structure and binding affinity of IL-6 family members. (**a**) Three-dimensional structure of four members of IL-6 cytokine family: LIF (leukemia inhibitory factor; PDB ID 1 emr), IL-6 (interleukin-6; PDB ID 1 alu), OSM (oncostatin M; PDB ID 1 evs) and IL-11 (PDB ID 4 mhl), showing the characteristic top (helix A, blue), top (helix B, light blue to green), down (helix C, green to yellow), down (helix D, orange to red) helix arrangement. (**b**) Receptor complexes of IL-6 cytokine family. The signaling receptor subunit gp130 is shared by all family members. IL-6 and IL11 have their specific subunits (IL6R and IL-11R, respectively). OSM can bind to OSMR and gp130 but also to LIFR. In turn, LIF and CT-1 (cardiotrophin-1) transduce intracellular signaling through binding to gp130 and LIFR. A CNTF (ciliary neurotrophic protein factor) receptor is normally composed of three complexes, namely CNTFR, LIFR and gp130, but can also signal through binding to IL-6R, LIFR and gp130.  
The cytokines can be encapsulated, adsorbed or conjugated into the nanocar system, and the nanometric size of these delivery systems increases the absorption of the drug by the cells, when compared to larger particles. One has to, however, take into account that these immunostimulatory agents need to be delivered in sufficient amounts to attain the desired effect. In addition, the nanocarriers are frequently detected as foreign by the immune system, which can lead to unwanted toxicity or even immune clearance. In this way, the immunotoxicity of the nanocarrier should be evaluated by measuring pro-inflammatory cytokine levels, which can be associated with adverse reactions and low therapeutic efficacy of nanoparticles, depending on the applied treatment [43]. To overcome this pitfall, nanoparticles can be functionalized to alter their surface properties and facilitate the attachment of a wide variety of molecules, such as receptor ligands, thereby increasing the bioactivity of these nanomaterials [44].
## 6 Calculation of Absolute Location Error (ALE)  
For a particular CR visible in an S1A image product, its azimuth and slant/ground range image pixel position needs to be predicted (estimated) as accurately as possible. To do this, the following steps are required:  
* The surveyed CR position is adjusted for product-time plate tectonic drift and SET, as described in the previous sections
* The relevant timing annotations are extracted from the product annotations; these include the azimuth zero-Doppler time stamps, the orbital state vectors, the near-range fast time, and the range and azimuth sample spacings.
* Range-Doppler geolocation e.g., as described in [19] and [20] is performed for the CR coordinate, giving a range and an azimuth time as the output.
* The resulting slant range prediction is corrected by adding the modeled PD, and the azimuth time is corrected by subtracting the bistatic residual.  
The above steps result in a range-azimuth _predicted_ position that can be compared to the position of the peak intensity in the image raster itself, _i.e._, the _measured_ CR position. Then, the ALE for that CR is given simply as:\[\mathrm{ALE}_{\mathrm{rg}}[\mathrm{samples}]=\mathrm{predicted\ range\ sample}-\mathrm{measured\ range\ sample} \tag{4}\]  
\[\mathrm{ALE}_{\mathrm{az}}[\mathrm{samples}]=\mathrm{predicted\ azimuth\ sample}-\mathrm{measured\ azimuth\ sample} \tag{5}\]  
All terms in Equations (4) and (5) are sub-sample (non-integral) values, with the CR peak intensity measured using 64-times FFT-oversampled complex input samples. The ALE, when expressed relative to the measured target positions, describes the degree to which the product annotations reflect the image sample positions. Of course, some noise remains from the (imperfect) model corrections, as well as from the actual measurement of the CR image position.
# Robust Transceiver Design for IRS-Assisted Cascaded MIMO Communication Systems
## 3 Achievable Rate (Full-Duplex Relay)
### Algorithm Description
Using Lagrangian multiplier, we have

\[\mathbf{H}_{R\uparrow}\mathbf{\Theta}\mathbf{H}_{IR}= \left(\sum_{r_{1}=1}^{K_{r}}\sum_{r_{2}=1}^{M}h_{RIr_{1},r_{2}} \left|r_{1}\right\rangle\!\!\left\langle r_{2}\right|\right)\left(\sum_{r_{3} =1}^{M}\theta_{r_{3},r_{3}}\left|r_{3}\right\rangle\!\!\left\langle r_{3} \right|\right)\left(\sum_{r_{4}=1}^{M}\!\!\sum_{r_{5}=1}^{K_{t}}h_{IRr_{4},r_ {5}}\left|r_{4}\right\rangle\!\!\left\langle r_{5}\right|\right) \tag{17}\]0 \[\mathbf{H}_{R\uparrow}\mathbf{\Theta}\mathbf{H}_{IR}= \left(\sum_{r_{1}=1}^{K_{r}}\sum_{r_{2}=1}^{M}h_{RIr_{1},r_{2}} \left|r_{1}\right\rangle\!\!\left\langle r_{2}\right|\right)\left(\sum_{r_{3} =1}^{M}\theta_{r_{3},r_{3}}\left|r_{3}\right\rangle\!\!\left\langle r_{3} \right|\right)\left(\sum_{r_{4}=1}^{M}\!\!\sum_{r_{5}=1}^{K_{t}}h_{IRr_{4},r_ {5}}\left|r_{4}\right\rangle\!\!\left\langle r_{5}\right|\right) \tag{17}\]1\[\mathbf{H}_{R\uparrow}\mathbf{\Theta}\mathbf{H}_{IR}= \left(\sum_{r_{1}=1}^{K_{r}}\sum_{r_{2}=1}^{M}h_{RIr_{1},r_{2}} \left|r_{1}\right\rangle\!\!\left\langle r_{2}\right|\right)\left(\sum_{r_{3} =1}^{M}\theta_{r_{3},r_{3}}\left|r_{3}\right\rangle\!\!\left\langle r_{3} \right|\right)\left(\sum_{r_{4}=1}^{M}\!\!\sum_{r_{5}=1}^{K_{t}}h_{IRr_{4},r_ {5}}\left|r_{4}\right\rangle\!\!\left\langle r_{5}\right|\right) \tag{17}\]2

Now notice that as \(\sum_{i}\log_{2}(1+\frac{\varepsilon}{\eta})\) is a constant, we have \(\frac{\partial\sum_{i}\log_{2}(1+\frac{\varepsilon}{\eta})}{\partial\sigma_{ \tau_{i}}^{2}}=0\) and \(\frac{\partial\sum_{i}\log_{2}(1+\frac{\varepsilon}{\eta})}{\partial\lambda}=0\). As a result, the optimum interference allocation for \(\mathbf{\gamma}_{r}^{\star}\) is the same as that of \(\mathbf{\gamma}_{r}^{\star}\). Similarly to the case of minimization, here we have \(\sum_{i}\varepsilon_{i}=\varepsilon\). In addition, we have \(\varepsilon_{i}\geq 0\) and there exists at least one \(i^{\prime}\) for which we have \(\varepsilon_{i^{\prime}}>0\). Finally, as \(f_{i}(x)=\log\left(1+\frac{\sigma_{1_{i}}^{2}x}{1+P\sigma_{2_{\rho(i)}}^{2}} \gamma_{\tau_{i}}\right)\) is a monotonically increasing function of \(x\), we conclude \(R_{\text{sr}}^{\text{FD}}(\mathbf{\gamma}_{s}^{\star})<R_{\text{sr}}^{\text{FD}}( \mathbf{\gamma}_{s}^{\star})\) which contradicts the first assumption of \(\mathbf{\gamma}_{s}^{\star}\) being the optimal source power allocation, and the proof is complete. ## Appendix D

First, we show \(R_{\text{sr}}^{\text{FD}}\) is a decreasing function of \(T\) and an increasing function of \(P_{s}\). It is sufficient to show \(\frac{dR_{\text{sr}}^{\text{FD}}}{dP_{s}}\geq 0\) and \(\frac{dR_{\text{sr}}^{\text{FD}}}{dT}\leq 0\).
## 5 Demonstration Use-Case
### Discussion and Lessons Learned  
The above experimental setup and scenarios of the use-case proved to be appropriate to demonstrate all the above six features of the MESS. Hence, system components could be (de-)activated in a plug-and-play manner. Production and internal logistics could speak the same language, just like legacy robotic systems and brand new HRC workcells. Production and logistics processes could be run both in the virtual and physical worlds, even in parallel. This opens new opportunities both for right-first-time system design and configuration, as well as anticipating and avoiding failure situations. In settings where humans are closely involved in production, such a service is essential, whereas its lack could be detrimental [20; 55].  
Figure 11: Process plan of the demonstrated use-case scenario.  
Figure 10: Schematic graph of the demonstrated use-case scenario.  
The implemented MESS and related (PAS-based) CPS resulted in a valid solution for an easier (re)configuration, as well as extensibility and management of the production and logistics processes, thanks to the conceptualized CPS common service model. This led to the conversion of isolated, legacy industrial equipment into I4.0 ready CPSs, which are capable of exposing their functionalities in terms of services. On the other hand, some CPSs evidenced specific issues. Factory line #1, for instance (no. 2 on Figure 8), could natively handle only a predefined number of _tasks_, and this number could not be changed dynamically during the operation. When parameterizing variables, moreover, they could only have read-only properties, due to system security and to avoid unwanted intervention. Nevertheless, by the protocol for writable variables, server-side and client-side writing were also allowed, which might cause unattended security risks. To overcome this, it was necessary to create cohesiveness between the field programmer level and the control programmer level. The establishing of the standardized synchronization of these programming levels requires further development and research work. It was also necessary, for safety reasons, to configure the network permissions in a way to restrict and route communication traffic only from accredited CPSs. After this initial setup, a variety of process plans were successfully carried out by the various CPSs, in a way that single production cells could be incrementally linked to much wider production processes.
# Practical Comparison Between the CI/CD Platforms Azure DevOps and GitHub
## 3 Feature-by-Feature Comparison
\begin{table}
\begin{tabular}{p{56.9 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Feature Category** & **Azure DevOps** & **GitHub** & **Key Differences and Considerations** \\ \hline Version Control & Supports Git and TFVC (Team Foundation Version Control) & Git-based only, optimized for distributed workflows & Azure DevOps is ideal for enterprises transitioning from legacy systems, GitHub is built for Git-native workflows. \\ \hline Repository Management & Integrated with enterprise project structures, designed for large teams & Designed for collaborative open-source development & Azure DevOps is suited for multi-repo enterprise projects, GitHub is collaboration-first with a developer-friendly UI. \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Feature-by-feature comparison with key differences and considerations.
# Device Reliability Analysis of NNBI Beam Source System Based on Fault Tree
###### Abstract
Although investigations into fault analysis systems for similar domestic and foreign equipment have been conducted [6, 7, 8], no dedicated fault analysis system tailored to one's own needs has yet been initiated. This study takes the megavolt-level NNBI beam source system as the research object and, for the first time, constructs a quantitative reliability model by combining the fault tree analysis (FTA) with the AutoFTA 3.9 platform, identifies weak links, quantifies the failure contribution weights of key components, and deduces calculation methods such as failure rates and uncertainty boundaries, so as to provide a quantitative framework for its reliability evaluation.
# Text Semantics-Driven Data Classification Storage Optimization
## 5 Experiments and Evaluation
### Experimental Setup
#### 5.1.3 Workload Characteristics  
To evaluate the performance of various solutions, we adopt a replay real-access record method. K-ear selects a set of 70 keywords, mainly including entertainment, season, work, and other aspects. Simultaneously, it crawls their access records from the Baidu index website for 2018 and generates experimental workloads based on this. However, this method has certain limitations, including a relatively small number of selected keywords and a short time span. We have borrowed from this method, refining and improving it. Specifically, we have augmented the keyword set to encompass 200 keywords (such as short skirt, tree planting, Spring Festival, skiing, Christmas, etc.), offering a more comprehensive and diverse representation. After classification using the semantic similarity algorithm, the classification results for the 200 keywords are: Spring: 27, Summer: 25, Autumn: 5, Winter: 29, Other: 113. Furthermore, we have extended the time span for our data collection, covering the period from January 2018 to December 2020. This extended time span allows for a deeper exploration of periodic patterns and trends in the data, providing a more solid foundation.  
There are seven key points to consider regarding the characteristics of the workload.  
1. _Workload format_. The workloads include vital data from access records, such as text, request size, storage location, and timestamp.
2. _Workload generation_. We organize the keyword access records monthly and calculate the ratio to the year's access records. This approach guarantees that the workload characteristics remain unaffected by the workload size.
3. _Number of I/O records_. The number of records affects the disk array's load. Generally speaking, the search volume for each feature keyword is huge. Therefore, we choose to optimize the experiment by limiting the total I/O records monthly.
4. _Access frequency_. Generally, each access is considered an I/O record.
5. _Data size distribution_. Prior studies [31; 32] observed that the data size shows a robust correlation with the access frequency, often conforming to a Zipf-like distribution. Hence, we guarantee the law for data size and access frequency in reverse order.
6. _Request size_. We set equal access sizes for the same keyword, which can be obtained from the data size and frequency.
7. _Request timestamp_. Since the total number of I/Os is kept at a fixed value, the timestamps need to be scaled down by the same multiplier.
## 3 Lattice Construction Algorithm
### Special-Case Workarounds and Continuity Fallback Mechanism
#### 3.4.1 Workaround for Coplanar and Almost Coplanar Nodes  
If there is a plane through the node, such that edges to all neighbouring nodes include small angles to that plane, the special-case workaround for coplanar and almost coplanar nodes is applied. For this node geometry, the occurrence of edges in the convex hull representation is not a suitable measure to detect struts that should be connected by a ruled surface. A more convenient measure is the corresponding edges' polar angle. Ordered according to the value of their polar angle, neighbouring struts are connected with \(G^{1}\)-continuous ruled surfaces. Additionally, surface patches of circular disks are constructed parallel to the plane of coplanarity above and below the node. On the basis of these patches, each strut is connected by a \(G^{1}\)-continuous ruled surface. Finally, the regions between the constructed transitions and struts are filled with \(G^{1}\)-continuous 6-patches. The corresponding construction stages are sketched in Figure 7.
## 1 Introduction
### Application of Trope for Defense Communication  
Tropospheric systems are specifically suited for military communications. A single tropo link, covering hundreds of kilometers, can replace multiple Line of Sight (LoS)communication stations required by armed forces to cover a certain area. This reduces installation and maintenance costs, security requirements, and the number of transmission frequencies required. Tropo systems have a narrow beam width of 1-2 degrees, making them difficult to intercept by enemy forces and less susceptible to jamming. They have a propagation delay of a few milliseconds, which is suitable for near-real-time communication. Narrow beams and negligible delay make tropo systems a better choice than satellite-based systems for moderately long-distance military communications. Moreover, many nations do not rely on satellites for critical communication due to the huge costs of operating their own satellites and the lack of operational control over satellites owned by other nations, especially during war.  
Today, troposcatter communication systems are used worldwide for military applications. In India, tropo systems are used by the armed forces to form an air defense communication network for situational awareness, wherein enemy aircraft movement is tracked by various sensors located at the borders and conveyed to strategically located weapon controllers. Transportable tropo systems were used by the US armed forces in 'Operation Enduring Freedom' for military logistics support by setting up communication links between forward bases and logistics hubs to convey requests for troops and supplies. Tropo systems can be used by the military for other applications like gathering and passing military intelligence, setting up command and control posts, and establishing robust data networks between various sensors and shooters (network-centric warfare). The use of software-defined radios (SDRs) in tropo systems will further boost their data rates, adaptability to changing atmospheric conditions, and resilience to electronic warfare (EW) by implementing frequency hopping tactics.