esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
For the first episode of dislocation, the technique most frequently mentioned in recent studies is the reconstruction or repair of the medial patellofemoral ligament using a mini-open approach and a graft stronger than the native medial patellofemoral ligament (MPFL) [11; 12]. According to recent studies, the success rate of MPFL reconstruction ranges from 80% to 95%, with most patients experiencing significant improvements in quality of life and knee function. However, the outcomes may vary based on factors, such as patient age, the presence of associated knee pathologies, and the timing of surgery [2; 13]. Common complications associated with MPFL reconstruction include graft failure, recurrent patellar dislocations, patellar fracture, stiffness, and infection. On the other hand, conservative treatment is more commonly associated with recurrences, leading to subsequent re-dislocations of the patella [13]. The fundamental step following surgical treatment is rehabilitation. There is no precise explanation of specific exercises or exercise parameters in the literature. Despite this, overall, it is necessary to recover the range of motion (ROM) and strengthen the quadriceps femoris muscle [14]. When comparing conservative and surgical treatment, numerous rating scales are used to help determine which treatment works best on a study sample. One of the most commonly used is the Kujala score, which evaluates anterior knee pain through a questionnaire assessing symptoms, functional limitations, and alignment-related issues. Other scales include the Tegner activity score, mean Norwich patellar instability score, and Cincinnati Knee Rating System, which measure physical activity levels, quantify patellar instability severity, and evaluate treatment outcomes in knee disorders [15; 16; 17]. This systematic review was performed to assess the outcomes of conservative versus surgical treatments for patellofemoral instability. We compared the effectiveness of these approaches by analyzing rehabilitation protocols and evaluating scores from previous research. Our objective was to determine the optimal treatment strategy by synthesizing and interpreting data from scientific studies on the subject.
## 3 Results
### SEM Analysis  
SEM analysis allows for a precise examination of a sample surface. However, in the case of concrete with TiO\({}_{2}\) nanoparticles, such analysis is difficult. In the study, a Secondary Electrons Secondary Ions Detector (SESI) and an InLens detector were applied. An InLens detector was used to map the surface of the sample, and the SESI detector was used for images with fine detail. Using these detectors, it was possible to make morphological studies of particles and agglomerates, although the chemistry of these particle had to be confirmed by EDS analysis. EDS mapping was therefore performed first to indicate areas with a higher and lower content of titanium. Morphological studies of both types of area were then carried out using secondary electrons techniques. This approach enabled an investigation of the content of titanium dioxide and the morphology of its particles. Results are presented in Figures 8-10. The obtained images confirmed the presence of TiO\({}_{2}\) particles in the studied sample. Titanium dioxide was present in the form of agglomerates with a diameter of 0.25-5 \(\upmu\)m. Titanium dioxide agglomerates were bonded to the compounds of the cement matrix.  
Figure 7: Concentration of NO\({}_{x}\) over time (cleaned sample, 300 W light source).  
Figure 8: SEMimage of the sample surface (SESI detector at ETH = 2.00 kV). (**a**) General view, (**b**) magnification of the selected area. Red arrows indicate TiO\({}_{2}\) agglomerates on the observed surface.  
Figure 10: (**a**) SEM image with indicated region of the X-ray analysis, (**b**) X-ray analysis of the indicated region.  
Figure 9: SEM image of the sample surface (InLens detector at ETH = 2.00 kV). (**a**) Morphology, (**b**) EDS maps of titanium in the presented region.  
For further analysis, scanning electron microscopy coupled with energy-dispersive spectrometry (SEM-EDS) was used to conduct quantitative X-ray analyses of the sample. To determine the places of X-ray analysis, an accurate sample EDS mapping of titanium was performed. The results of this analysis are presented in Figure 10. The obtained images and results indicate a steady distribution of TiO\({}_{2}\) agglomerates.
# IEEE 802.11 ax OFDMA Resource Allocation with Frequency-Selective Fading
## 6 Power Control
### Power Supplement  
Another possible approach to deal with the sudden worsening of the channel conditions caused by the fading is to maintain some reserve in transmission power. We denote this approach as Power Supplement and implement it as follows. We define a parameter \(P_{sup}\). With Power Supplement, when the AP sends a \(Target_{RSSI}\) parameter to a STA,it increases this parameter by \(P_{sup}\). As a result, according to (9), the STA increases its transmission power by \(P_{sup}\) (provided that it does not reach the power limit).  
The idea behind this approach is that when the AP schedules transmission for a STA at a given MCS, it expects that the power of the STA signal at the AP will be sufficient for reliable reception of the signal at the considered MCS. With Power Supplement, we increase the received power but do not change the MCS, so that if the fading leads to a sudden drop of the received power, it still will be enough for correct reception.  
Note that such an approach can be combined together with the CSI Adaptation Algorithm described earlier.
# The Standardization Method and Application of the BIM Model for Interchanges
## 3 Classification and Coding of BIM Components for Interchanges
### Method of Classification of Components  
Scientific and reasonable classification of interchange BIM components can standardize and provide an orderly count of the various components of interchanges, and greatly improve the process of standardization of interchange BIM components. The classification of interchange BIM components should not only be based on the attributes and characteristics of the information contained in the component model, but also should be associated with the BIM level. Combined with the characteristics of interchanges, the BIM components of interchanges should include the following: ramp components, variable speed lane components, auxiliary lane components, collector components, interweaving area components, nose end construction components, ramp end planar intersection components, etc., as shown in Table 1.  
Figure 2: Classification of interchange components.  
To distinguish and categorize interchange BIM components more systematically, it is also necessary to choose a suitable classification method. Information classification mainly includes three basic methods: line classification, surface classification, and hybrid classification [27]. The line classification method gradually classifies the classification object into several hierarchical categories according to the attributes or characteristics of the classification object, and finally into a hierarchical and gradually unfolded system. The surface classification method selects several attributes or characteristics to divide the classification object into categories without affiliation, and then continuously divides the selected categories into a mutually independent classification system. The mixed classification method combines the above two methods. The mixed classification method generally uses one of the above two methods as the main means of classification and the other method as a supplementary classification method.  
In interchanges, a BIM component often contains multiple attributes or features. For example, an auxiliary lane in an interchange usually contains multiple aspects, such as design speed, cross-section type, and geometric indicators, which are suitable for classification using the surface classification method. At the same time, considering the principle of compatibility in the classification of components, the classification method of interchange BIM components should be consistent with the classification methods in the existing relevant national standards. The use of the facet classification method is beneficial because of the addition of interchange component attributes and features, without the need to determine the final grouping in advance, and the later completion is also convenient for computer retrieval and suitable for computer management.
## 3 Methodology
### System Setup  
The system setup consists of two main parts: system initialization and deployment. In the system initialization phase, all the necessary parameters, such as the private key and public key, are generated and stored in each machine. This involves implementing a key generation mechanism to generate the required cryptographic keys. In the deployment phase, encryption and decryption mechanisms are employed to ensure secure communication within the network-based manufacturing system. The process of system initialization and deployment is illustrated in Figure 10.  
The NTRUEncrypt encryption and decryption mechanism is illustrated in Figure 11. In this mechanism, the public key of any manufacturing cell node in the NBMS is known to the other nodes, while the private key is kept confidential. The encrypted message, also known as the ciphertext, comprises the receiver's public key encrypted with the sender's message. The decrypted message, referred to as the plaintext, is obtained by decrypting the ciphertext using the private key.  
NTRUEncrypt-Secured IoT-Enabled NBMS  
Figure 12 illustrates the secure communication process between the user and the respective machine in the IoT-enabled network-based manufacturing system. The user of one machine sends an encrypted message to another machine through the internet, and the receiver machine decrypts the message to obtain the original plaintext. This process ensures secure communication using the NTRUEncrypt encryption and decryption mechanism, as depicted in Figure 11.
## 3 UT-Based CLAT
### Neighbor Measurement Update and Target Information Fusion  
When two robots \(i\) and \(j\) are within a given range, a relative measurement is taken, denoted as \(\mathbf{z}_{ij,k}\), and a communication link between the two robots is established. The target update process is as follows. First, the covariance between two robots \(\mathbf{P}_{ij}^{-}\) is recovered according to Equation (6). Similar to the target measurement update process, we define the augmented state prior to the measurement update as  
\[\mathbf{\hat{x}}_{k}^{c-}=\begin{bmatrix}\  
where  
\[\mathbf{S}_{i,k} =\begin{bmatrix}\mathbf{\mathcal{H}}_{i,k}&\mathbf{\mathcal{H}}_{j,k}\end{bmatrix} \begin{bmatrix}\mathbf{P}_{i,k}&\mathbf{P}_{ij,k}\\ \mathbf{P}_{ij,k}^{\top}&\mathbf{P}_{j,k}\end{bmatrix}\begin{bmatrix}\mathbf{ \mathcal{H}}_{i,k}&\mathbf{\mathcal{H}}_{j,k}\end{bmatrix}^{\top}+\mathbf{R}^{c},\] \[\mathbf{K}_{i,k} =(\mathbf{P}_{i,k}\mathbf{\mathcal{H}}_{i,k}+\mathbf{P}_{ij,k}\mathbf{ \mathcal{H}}_{j,k})\mathbf{S}_{k}^{-1},\] \[\mathbf{K}_{j,k} =(\mathbf{P}_{ji,k}\mathbf{\mathcal{H}}_{i,k}+\mathbf{P}_{j,k}\mathbf{ \mathcal{H}}_{j,k})\mathbf{S}_{k}^{-1}.\]  
After the relative measurement update, the correlation \(\mathbf{P}_{ij,k}\) is decomposed again as two multiplicative parts \(\sigma_{ij,k}\) and \(\sigma_{ij,k}\) according to Equation (22). Then, \(\sigma_{ij,k}\) and \(\sigma_{ji,k}\) are stored in \(i\) and \(j\), respectively.  
The relative measurement update process also involves the correlation term \(\mathbf{P}_{il,k},l\in\mathcal{V},l\
eq i,j\). Formally, the \(\mathbf{P}_{il,k}\) should be updated as  
\[\mathbf{P}_{il,k}=(\mathbf{I}-\mathbf{K}_{i,k}\mathbf{\mathcal{H}}_{i,k})\mathbf{ P}_{il,k}-\mathbf{K}_{i,k}\mathbf{\mathcal{H}}_{j,k}\mathbf{P}_{jl,k}.\]  
The correlation term \(\mathbf{P}_{jl,k}\) is not available to robot \(i\). To reduce the overall communication and avoid communication with \(l\), the split term \(\sigma_{il}^{-}\) in robot \(i\) is instead updated in an approximate form, similar to the process in [9], as below:  
\[\sigma_{il,k}=\mathbf{P}_{i,k}(\mathbf{P}_{i,k}^{-})^{-1}\sigma_{il,k}^{-}. \tag{28}\]  
In addition to the measurement update, the target beliefs \(\{\hat{\mathbf{x}}_{t_{i},k},\mathbf{P}_{t_{i},k}\}\) and \(\{\hat{\mathbf{x}}_{t_{j},k},\mathbf{P}_{t_{j},k}\}\) are fused simultaneously. As a matter of fact, the correlation between the two estimates is unknown owing to the unknown target-robot correlation. Again, the conservative CI algorithm can be used as below:  
\[\mathbf{P}_{t,k} =\left(w_{2}(\mathbf{P}_{t_{i},k})^{-1}+(1-w_{2})(\mathbf{P}_{t_ {j},k})^{-1}\right)^{-1}, \tag{29}\] \[\hat{\mathbf{x}}_{t,k} = \tag{30}\]  
The weight \(w_{2}\) can be determined according to [11]. The fused results are then stored in both robots \(i\) and \(j\).
## 3 Results and Discussion
### Optimization of Single HAP Conditions
#### 3.1.3 Shear Rate  
Shear rate plays an important role in obtaining particle sizes with low MPS during the antisolvent process. Figure 3c showed that a notable reduction of MPS of spiramycin occurred when the shear rate increased from \(100\) to \(5000\) rpm. By contrast, a shear rate over \(5000\) rpm had only a weak influence on the MPS. It is perfect that the micromixing time is less than the nucleation time, which plays a crucial role in preparing ultrafine particles with uniform particle size distribution [38]. A fast nucleation rate is conducive to producing drugs with smaller particles [36]. A higher shear rate can lead to the materials  
Figure 3: The effects of each parameter on the MPS of micronized spiramycinin (**a**) precipitation time; (**b**) precipitation temperature; (**c**) shear rate; (**d**) volume ratio of the antisolvent to solvent; (**e**) dripping speed of the solvent adding into the antisolvent; and (**f**) spiramycin concentration. Each value represents means \(\pm\) SD (\(n=6\)).  
being mixed more homogeneously, which contributes to creating a uniform space distribution of concentration and improves the micromixing between the multiphases. Moreover, the homogenate at a high shear rate can break the agglomeration between particles; thus, 5000 rpm was considered as the optimum shear rate.
# A Maximum-Entropy Fuzzy Clustering Approach for Cancer Detection When Data Are Uncertain
## 3 Model and Algorithm
### Optimization Problem  
Let \(\mathbf{X}\) be a \(n\times J\) interval-valued data matrix. Given the distance measure shown in Equation (2), in which we assume that the weights (i.e., \(w_{c}\) and \(w_{r}\)) are objectively computed during the clustering process, we can classify observations within a fuzzy framework, by means of the entropy-based fuzzy clustering (EFC-ID) model, characterized as follows:  
\[ \begin{split}\text{min}& J_{\text{EFC}-ID}(\mathbf{U}, \mathbf{\tilde{X}},w)=\sum_{i=1}^{n}\sum_{g=1}^{k}u_{ig}\Big{[}w_{c}^{2}d^{2}( \mathbf{c}_{i},\mathbf{c}_{g})+w_{r}^{2}d^{2}(\mathbf{r}_{i},\mathbf{r}_{g}) \Big{]}+\\ &+p\sum_{i=1}^{n}\sum_{g=1}^{k}u_{ig}log(u_{ig})\\ \text{s.t.}&\sum_{g=1}^{k}u_{ig}=1,u_{ig}\geq 0,\\ & w_{c}\geq w_{r}\geq 0,w_{c}+w_{r}=1.\end{split}  \tag{3}\]  
where \(u_{ig}\) indicates the membership degree of the \(i\)-th unit in the \(g\)-th cluster; \(d^{2}(\mathbf{\tilde{x}}_{i},\mathbf{\tilde{x}}_{g})\) is the squared version of Equation (2) between the \(i\)-th unit and the centroid in the \(g\)-th cluster; \(\mathbf{c}_{i}\) and \(\mathbf{r}_{i}\) are the centers and radii of the \(i\)-th unit, respectively; \(\mathbf{c}_{g}\) are the centroids of the centers and radii in the \(g\)-th cluster, respectively; \(p\sum_{i=1}^{n}\sum_{g=1}^{k}u_{ig}log(u_{ig})\) is the _fuzzy entropy function;_\(p\) is a weight factor, called _degree of fuzzy entropy_, similar to the weight exponent \(m\) used in the fuzzy \(k\)-means approach and represents the uncertainty associated with each statistical unit which is defined as the Shannon entropy [22; 23; 24]. To simplify things, we can set \(w_{c}=(1-w)\) and \(w_{r}=w\). In this way, the _normalization condition_ is satisfied and the _coherence condition_ turns into \(0\leq w\leq 0.5\). Then, the objective function became:  
\[ \begin{split}\text{min}& J_{\text{EFC}-ID}(\mathbf{U}, \mathbf{\tilde{X}},w)=\sum_{i=1}^{n}\sum_{g=1}^{k}u_{ig}\Big{[}(1-w)^{2}d^{2}( \mathbf{c}_{i},\mathbf{c}_{g})+w^{2}d^{2}(\mathbf{r}_{i},\mathbf{r}_{g}) \Big{]}+\\ &+p\sum_{i=1}^{n}\sum_{g=1}^{k}u_{ig}log(u_{ig})\\ \text{s.t.}&\sum_{g=1}^{k}u_{ig}=1,u_{ig}\geq 0,\\ & 0\leq w\leq 0.5\end{split}  \tag{4}\]By solving the constrained quadratic minimization problem shown in Equation (4) _via_ Lagrangian multiplier method, we obtain the optimal solutions \(u_{ig}\) and \(w\). In particular, by considering the following Lagrangian function:  
\[ \begin{split} L_{m}(u_{ig},\lambda,w)&=\sum_{i=1}^{n} \sum_{g=1}^{k}u_{ig}\Big{[}(1-w)^{2}d^{2}(\mathbf{c}_{i},\mathbf{c}_{g})+w^{2}d ^{2}(\mathbf{r}_{i},\mathbf{r}_{g})\Big{]}+\\ &+p\sum_{i=1}^{n}\sum_{g=1}^{k}u_{ig}log(u_{ig})-\lambda\left( \sum_{g=1}^{k}u_{ig}-1\right),\end{split}  \tag{5}\]  
and setting the first partial derivatives with respect \(u_{ig}\) and \(\lambda\) equal zero, we obtain  
\[ \begin{split}\frac{\partial L_{m}(u_{ig},\lambda,w)}{\partial u _{ig}}=0\Leftrightarrow&\Big{[}(1-w)^{2}d^{2}(\mathbf{c}_{i}, \mathbf{c}_{g})+w^{2}d^{2}(\mathbf{r}_{i},\mathbf{r}_{g})\Big{]}+\\ &+p(log(u_{ig})+1)-\lambda=0,\end{split}
###### Abstract  
This study proposes a framework for remote identification and tracking of cattle movement based on wireless sensor networks (WSN), mobile communication, and unmanned aerial vehicles (UAVs). The proposed framework can be implemented for tracking cattle movement at the village level and extended to harsh terrain when recovering stolen animals in case of cattle rustling or theft. The system works by identifying and tracking the desired animal location and sending periodic location data at regular intervals to a database as well as availing the specific animal's current location on demand through the Internet and text messages. The proposed cattle tracking and recovery system (CTRS) consists of a rumen sensor module, a WSN control unit, a Worldwide Interoperability for Microwave Access (WiMAX) gateway, WiMAX base stations, and a data center. Availing timely information about the location of the stolen animals could facilitate quick recovery of the animals while ensuring the safety of the security personnel involved in the recovery process. Speedy recovery of stolen animals also defeats the purpose of cattle rustling as the rustlers do not have the chance to use the animals for their intended purposes. The proposed system was tested through a MATLAB simulation experiment.  
wireless sensor network; mobile communication; cattle tracking; cattle rustling +
Footnote †: journal: methodologies
# The Microwave Temperature and Humidity Profiler: Description and Preliminary Results
## 4 TI3 GER Campaign Measurements
### Radio Frequency Interference  
It is also important to note in flight #8 (Figure 14) the increased noise that is not present in other areas or flights in the lower frequency absorption line (~54 GHz), notably while facing upwards while the airplane is at a high altitude. Flight #8 was conducted from Kona to the Equator and back. Preliminary analysis of these results determined the potential presence of Radio Frequency Interference (RFI) coming from the coordinated inter-satellite link channel from the FCC (see 5.556A) [21]. The frequencies between 54.25 and 56.9 GHz are reserved for inter-satellite links between geostationary satellites.
# Opportunities and Challenges for Error Control Schemes for Wireless Sensor Networks: A Review
## 2 Error Control Scheme (ECS)
### Other Techniques  
In addition to ARQ, FEC, HARQ and channel-adaptive techniques, researchers have utilised other various techniques such as Bit-Interleaved Coded Modulation (BICM), network coding, and residual number system for error correction and energy efficiency analysis in WSN. In BICM [43], modulator and channel encoder are separated by a bit-level interleaver. The BICM technique is suitable for fading channel and adapted for Wireless Local Area Network (WLAN) standard (IEEE 802.11a/g) [44] and broadband wireless access (IEEE 802.16) [45]. We will perform a brief review of BICM used in WSN. However, we are not focusing review on network coding and the residual number system in this paper.
## 3 Results and Discussion
### Comparison of Downscaled Outcome with Modeled Results in Three and Thessaly
In that way, the Mean Absolute Error (\(MAE\)), which measures the distance between observed (\(O_{l}\)) and predicted (\(P_{l}\)) values of a dataset of size \(N\) was computed [23]:  
\[MAE=\frac{1}{N}\sum_{i=1}^{N}|P_{i}-O_{i}| \tag{7}\]  
as well as the Scaled Root Mean Squared Error (\(R^{*}\)), where \(\sigma_{o}\) is the standard deviation of observed values:  
Figure 11: Intercept (mm) and model coefficients of precipitation time lags (mm/mm) for Thessaly and Thrace regions computed using the 2005 to 2015 time series in Thrace and Thessaly. (Graphical scale covers only the provided location map. Pixel size for computed slope and intercept is \(\sim\)10 km \(\times\) 10 km). \[R^{*}=\frac{RMSE}{\sigma_{o}}=\frac{\sqrt{\frac{1}{N}\sum_{i=1}^{N}(P_{i}-O_{i})^{2 }}}{\sigma_{o}} \tag{8}\]  
The downscaled TWSA compared to SWAT-modeled TWSA demonstrated _MAE_ 24.0 mm and 16.5 mm for Thrace and Thessaly basins, whereas, for the original GRACE time series, the _MAE_ ranges from 51.5 mm (Thrace) to 47.0 mm (Thessaly). Regarding the comparison with the ERA5 TWSAs, _MAE_ decreased from 30.6 to 13.4 mm in Thrace and from 28.6 to 16.9 in Thessaly. Analogous improvement was observed while analyzing the \(R^{*}\) index. Therefore, compared to SWAT TWSAs, \(R^{*}\) decreased from 0.83 to 0.41 in the Thrace area for the downscaled TWSA, and from 0.78 to 0.28 in Thessaly. Compared to ERA5 TWSAs, the \(R^{*}\) index was also improved in the downscaled product, from 0.59 to 0.25 in Thrace and from 0.48 to 0.30 in Thessaly. In all examined metrics, the outcome indicates that there is a considerable improvement in the downscaled TWSA in both areas. Observing both graphs in Figure 12, it is evident that the downscaled TWSAs better represent the timing of TWSA, as a result of the incorporation of the auxiliary variable, i.e., GPM IMERG, at the detected lags, i.e., lag 1 for Thrace and lag 1 and 2 for Thessaly. The above-presented results highlight the advantages of the analysis at the basin scale, where time lags are customized based on the cross-correlation analysis for each basin separately. The close performance of the downscaled and modeled TWSA indicate that the downscaled product can possibly contribute to the constraining of hydrological models, which has been reported to offer a substantial improvement to model results [16]. Figure 12: Comparison of TWSA (mm) from SWAT model and ERA5-Land, GRACE original and GRACE downscaled TWSA from 2005 to 2015 in two Water Districts in Greece. (**a**) Graph corresponds to two adjacent basins in Thrace Water District. The (**b**) graph refers to a basin in the Thessaly Water District.