esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 CSES Optimization Framework with Dual-Time-Scale Coordination
### Operational Models for CSES  
#### 3.2.1 Operational Constraints  
Unlike conventional ESS constraints, CSES constraints are more flexible. CSES allows for varying capacities and enables the sharing of stored energy across subnetworks. These constraints can be categorized into power constraints, SOC constraints, and coordination constraints.  
A. Coordination Constraints:  
The net power exchange between the CSES and the AC subnetworks is given by Equation (32):  
\[P_{t}^{\text{CSES}}=P_{t}^{\textit{dch},\text{CSES}}-P_{t}^{\textit{dh},\text{ CSES}} \tag{32}\]  
where \(P_{t}^{\textit{ch},\text{CSES}}\) and \(P_{t}^{\textit{dch},\text{CSES}}\) are the CSES charging and discharging power at time \(t\).  
The distribution of this power among subnetworks must satisfy Equation (33):  
\[P_{t}^{\text{CSES}}=\sum_{\epsilon\in E}{(P_{\epsilon,t}^{\textit{dch},\text{ CSES}}-P_{\epsilon,t}^{\text{ch},\text{CSES}})},\forall t\in T \tag{33}\]  
where \(P_{\epsilon,t}^{\textit{ch},\text{CSES}}\) and \(P_{\epsilon,t}^{\textit{dch},\text{CSES}}\) are the charging and discharging power allocated to subnetwork \(e\) at time \(t\).  
B. SOC Constraints:  
SOC of the CSES changes according to Equation (34):  
\[E_{t+1}^{\text{CSES}}=E_{t}^{\text{CSES}}+\eta_{\textit{ch}}P_{t}^{\textit{ch},\text{CSES}}\Delta t-\frac{P_{t}^{\textit{dch},\text{CSES}}\Delta t}{\eta_{ \textit{dch}}},\forall t\in T \tag{34}\]  
where \(E_{t}^{\text{CSES}}\) is the SOC of the CSES at time \(t\).  
SOC of the CSES must be maintained within safe operational limits:  
\[E^{\textit{min},\text{CSES}}\leq E_{t}^{\text{CSES}}\leq E^{\textit{max}, \text{CSES}},\forall t\in T \tag{35}\]  
Figure 1: AC/DC distribution system with CSES.  
Additionally, to ensure sustainable cyclic operation, the final SOC should return to the initial SOC:  
\[E_{T}^{\text{CSES}}=E_{0}^{\text{CSES}} \tag{36}\]  
* Power Constraints: The charging and discharging power of the CSES must satisfy Equations (37)-(39): \[0\leq P_{e,t}^{ch,\text{CSES}}\leq\mu_{e,t}^{ch,\text{CSES}}P_{e}^{ch,max, \text{CSES}},\forall e\in E,\forall t\in T\] (37) \[0\leq P_{e,t}^{ch,\text{CSES}}\leq\mu_{e,t}^{ch,\text{CSES}}P_{e}^{ch,max, \text{CSES}},\forall e\in E,\forall t\in T\] (38) \[\mu_{e,t}^{ch,\text{CSES}}+\mu_{e,t}^{ch,\text{CSES}}\leq 1,\forall e\in E,\forall t\in T\] (39)  
where \(\mu_{e,t}^{ch,\text{CSES}}\) is the charging state 0-1 variable, 0 means not charging, 1 means charging; and \(\mu_{e,t}^{ch,\text{CSES}}\) is the discharging state 0-1 variable, 0 means not discharging, 1 means discharging. Equation (39) ensures that each subnetwork cannot charge and discharge simultaneously, although the CSES as a whole may be charging with some subnetworks while discharging with others at the same time.  
The total charging and discharging power allocated to all subnetworks must not exceed the CSES capacity:  
\[\sum_{e\in E}P_{e,t}^{ch,\text{CSES}}\leq P^{ch,max,\text{CSES}} \tag{40}\]  
\[\sum_{e\in E}P_{e,t}^{ch,\text{CSES}}\leq P^{ch,max,\text{CSES}} \tag{41}\]
## 3 Solution Method for Orbital Pursuit-Evasion-Defense Game
### Hybrid Numerical Method
#### 3.2.2 Multiple Shooting Method  
In order to better illustrate the application of the multiple shooting method in this problem, a new state vector is defined:  
\[\Omega(t)=[\mathbf{Z}_{\rm PE}(t),\mathbf{Z}_{\rm DP}(t),\,\Lambda_{\rm E}(t),\,A_{ \rm D}(t)] \tag{32}\]  
Substituting Equation (32) into Equation (28), the system equations can be expressed as follows:  
\[\dot{\Omega}(t)=f(t,\,\Omega(t)) \tag{33}\]The multiple shooting method transforms the TPBVP into a series of initial value problems. The specific steps are given as follows:  
1. Divide the time interval \([0,t_{\mathrm{f}}]\) into \(\mathrm{m}\) subintervals, and \(t_{\mathrm{k}}\) (\(k=0,\,\cdots,\,\mathrm{m}\)) represents the boundary points of subintervals, which satisfy \(0=t_{0}<t_{1}<\cdots<t_{\mathrm{m}}=t_{\mathrm{f}}\).
2. For each subinterval \([t_{\mathrm{f}},t_{\mathrm{i+1}}]\) (\(i=0,\,\cdots,\,\mathrm{m}-1\)), consider the initial value problem: \(\dot{\Omega}(t)=f(t,\,\Omega(t))\), \(\Omega(t_{i})=s_{i}\), where \(s_{i}\) is the initial value of the problem.
3. Calculate the initial guess by the multi-objective genetic algorithm.
4. Solve the initial value problem on each subinterval to obtain the solution \(\Omega(t,\,t_{\mathrm{f}},\,s_{i})\).
5. Determine whether the condition \(\Omega(t_{\mathrm{i+1}},\,t_{\mathrm{i}},\,s_{i})=s_{i+1}\) and boundary conditions (29) and (30) are satisfied. If not, use the Newton method to modify the initial value and return to step 4. If the conditions are satisfied, the solution of the TPBVP is obtained successfully.  
We point out that the accuracy of the initial guess value affects the solution obtained by the multiple shooting method. If the accuracy of the initial guess value is not enough, the convergence point may not be the desired solution. Moreover, it may increase the number of iterations and prolong the calculation time.
## 5 Implementation
### Architectural Components
#### 5.1.1 Backend Servers  
The inclusion of a backend server in our architecture is critical for supporting the cost-effectiveness of blockchain technology. This decision was based on a number of technological and economic factors as not all information needs to be stored on the blockchain. The blockchain's immutability and distributed nature make it ideal for storing data that require verification and audit trails. However, the cost of storing massive amounts of data  
Figure 2: Multilayer blockchain architecture [8].  
on the blockchain is significant. This cost, also known as 'gas fees', increases with the amount of data stored. Hence, the efficient use of blockchain storage is critical for cost management. Furthermore, accessing data stored on the blockchain is not free of charge. Reading information, while less resource-intensive than writing data, still incurs a fee in many blockchain implementations. This aspect emphasises the importance of being selective when storing data on the blockchain. The architecture proposes using the blockchain as a repository for data references, with the actual data stored off-chain. This approach leverages the blockchain's strengths in data integrity and verifiability while lowering the storage costs and technical constraints associated with direct data storage on the blockchain. Another major concern is the management of sensitive information. Blockchain data are inherently transparent, and once recorded, they are unchangeable. While these characteristics improve auditability and trustworthiness, they are not suitable for storing private or sensitive data. As a result, the architecture carefully separates sensitive data and stores them off-chain to ensure confidentiality and compliance with data privacy regulations. The strategic integration of backend servers into our architecture is more than a technical preference; it is a requirement driven by the blockchain's inherent characteristics and the economic realities of its application. This design choice makes our solution scalable, secure, and cost-effective.
## 4 Results and Discussion
### Visualization  
For visualization of the sample data in feature space, principal component analysis (PCA [59]) was applied to the fusion feature vectors (the \(1\times 3\)C vectors used as input to the final classifier) to reduce their dimensionality. Such visualization can provide insight into the cause of any classification errors (e.g., the presence of outliers within a class, or the overlap of samples between classes). In order to plot the feature representation of the samples, the feature vectors are reduced to three or fewer dimensions even though the vectors are already of low dimensionality. The scores for the first three principal components are plotted for the New Mexico soils in Figure 3A and for the military installation soils in Figure 3B. While overlap between different classes in reduced-dimension feature space does not necessarily indicate classification will be poor, clear separation between the classes is indicative that classification accuracy will likely be high. For the New Mexico soils, the fused feature vectors demonstrate clear separation for all the classes except the Upper and Lower Socorro Canyon sites, suggesting that multiple analyzer data fusion will result in high classification accuracy. Similarly, for the military installation soils, the samples in each class generally show visual separation from the samples from other classes.  
Figure 3: Principal component score plots of the fusion feature vectors for the New Mexico soil suite (**A**) and the military installation soil suite (**B**).  
For the New Hampshire soil suite, the samples separate clearly for the discrimination of the Lebanon and Belmont sampling locations (Figure 4A); however, a few class outliers exist for the intra-location samples at the Belmont site (Figure 4B). The outlier observed for Pit 2 is the deepest soil layer from this pit. Similarly, the two outliers observed for Pit 1 are the deepest and shallowest layers from this pit. Thus, as might be expected, soil depth has an impact on the measured spectra and may confound discrimination between sites.
# Rheumatoid Arthritis Diagnosis: Deep Learning vs. Humane
## 2 RA Diagnosis Methods: Modernism vs. Traditionalism
### The Physician Method
#### 2.3.4 X-ray Findings  
RA has a predilection for MCP and PIP (proximal interphalangeal) joints, ulnar styloid and triquetrum (Figure 9). DIPs (distal interphalangeal joints) are spared.  
Figure 8: Typical CDAI activity index template.  
Typical X-ray findings in RA patients include marginal erosions, symmetrical joint space narrowing, subchondral cyst formation, subluxation causing ulnar deviation of MCP joints or boutonniere and swan neck deformities. Other typical X-ray findings in RA patients are hitchhiker's thumb deformity, scapholunate dissociation, ulnar translocation, ankylosis (complete loss of the joints space) and scallop sign: erosion of the ulnar aspect of the distal radius, which may be predictive of extensor tendon rupture (Vaughan-Jackson syndrome).  
Bone erosions, especially at the bare areas of joints (joint areas that are not covered with cartilage), are common for RA and are easily detectable with conventional X-rays [40].  
There are many scoring systems that are used when evaluating an X-ray of a patient with RA. The most widely used nowadays is the modified [PERSON]/[PERSON] [13] scoring system.  
The original Sharp method assessed 27 joints in each hand and wrist, with each joint being given a separate score for joint space narrowing and erosions. The Sharp score focused on the hands and wrists (evaluation of 17 areas for erosions and 18 areas for joint space narrowing) and [PERSON] added the feet in these evaluations, a modification that was also used by [PERSON]. Because of their similarities, these radiographic scoring systems are known as \"modified Sharp methods.\" In modified Sharp scoring systems, each joint is given a score for joint space narrowing and another score for erosions. Fifteen sites in each hand and wrist and six joints in each foot are examined for joint space narrowing on a scale of 0 to 437. Joint space narrowing for each joint can range from 0 to 4:0 indicates no narrowing, 1 represents minimal narrowing, 2 indicates loss of 50% of the joint space, 3 indicates loss of 75% of the joint space, and 4 represents a complete loss of the joint space. The erosions are counted individually, usually at 16 sites in each hand and wrist and six sites in each foot. The erosion score per joint of the hands can range from 0 to 5. Erosions are scored 1 if they are discrete but clearly present and 2 or 3 if they are larger, depending on the surface area of the joint involved. A score of 4 is given if the erosion is large and extends over the imaginary middle of the bone. A score of 5 is given if a complete collapse of the joint is present or if the full surface of the joint is affected. In each joint, individual  
Figure 9: Bones and joints of the human hand, DIP—distal interphalangeal joint; PIP—proximal interphalangeal joint; IP—interphalangeal joint; MCP—metacarpophalangeal joint; CMC—carpo-metacarpol joint [39].  
erosions are summed up to a maximum of 5. The maximal erosion score for each hand is thus 80, considering the16 areas for erosions per hand [41].
## 2 Antenna Design
### MIMO Antenna Configuration  
The S-shape antenna was extended to four-element MIMO configuration. The total size of the antenna was kept at 24 mm \(\times\) 24 mm. The MIMO antennas are arranged orthogonal to each other. Additionally, as in MIMO antennas, the isolation values play vital role.  
Therefore, in order to enhance isolation and decrease the mutual coupling among radiating elements, an isolating structure is introduced in between four radiating elements. The proposed isolating structure consists of a tilted square small patch with extended arms at each side. Figure 5 shows the MIMO antenna design with isolating structure.
# Car Bumper Effects in ADAS Sensors at Automotive Radar Frequencies
## 1 Introduction  
The automotive industry is evolving into increasingly autonomous-ready cars [1]. The agents of change are active safety systems based on cameras, radars, LIDARs (Laser Imaging Detection and Ranging) and ultrasonic sensors. They can detect people and objects in the surroundings of the vehicle, allowing its central processing system to react accordingly [2]. The advantage of radar sensors, which makes them more important compared to other systems, is their capability to operate under adverse weather conditions [3]. Furthermore, they are more cost-effective than, for example, LIDAR systems [4]. In the future, the integration of these on-board Advanced Driving Assistance Systems (ADAS) into a wide network of Intelligent Transport Systems (ITS) [5] will increase the security of our roads and streets. This will reduce the probability of traffic accidents and, consequently, minimize the number of victims. Thus, ADAS represents the first link in the electronic security chain configured by ITS, an advanced communication system that provides links and information among vehicles, infrastructures and people [6].  
Current automotive radar frequency bands range from 76 to 81 GHz [7; 8; 9], corresponding to wavelengths between 3.94 and 3.70 mm. Formerly, frequencies ranging from 24 to 26 GHz were also considered, which were then repurposed for 5G mobile communications at millimeter-wave frequencies [10]. Moving the radar to higher frequencies gives rise to advantages such as larger bandwidths, better spatial resolution and reduced size.  
Radar sensors can be mounted behind the emblem, the front grille, or integrated behind or within the bumper and covered with a radome where necessary [11; 12]. Placing sensors behind the bumpers reduces the codependency in the design of both elements, giving the designer more degrees of freedom. A more independent design of both the radar and bumper allows more flexibility in the size of the radar equipment (including the antennas) and improves the overall aesthetic of the bumper [12]. This is desirable due to the increasing number of radars and other sensors that are mounted in various places all around the vehicles. Nevertheless, the impact of transmitting an electromagnetic signal via a bumper in the automotive radar frequency band must be analyzed, as has been carried out in the past for radome emblems [13; 14].  
With that motivation, we performed a set of measurements to characterize the signal attenuation caused by different samples of car bumpers. This paper is organized into six sections. After this introduction, Section 2 provides an overview of the evolution of automotive radar. In Section 3, we provide a summary of common car bumper materials, paints and finishes, and we introduce the specific characteristics of the bumper samples we have analyzed. Then, in Section 4, the experimental setup and the measurement procedures are described. The measurement results are discussed in Section 5, and finally, we summarize the main conclusions in Section 6.
## 3 Deficit Round Robin with Fragmentation (DRRF) (for Best Effort Traffic)
### Throughput Fair Allocation  
In traditional wired networks, the simple scheduling algorithms, _i.e._, GPS, and DRR, have the assumption that the link capacity is constant over time and location. However, it is not always the case in wireless networks, particularly IEEE 802.16e Mobile WiMAX Networks. In Mobile WiMAX, to support a reliable transmission and throughput optimality, various modulation and coding schemes (MCSs) are allowed. The MCS levels are determined by CINR (Carrier to Interference-plus-Noise Ratio), typically sent back over a CQI&ACK channel, and the desired BLock Error Rate (BLER) [1,2].  
Thus, to overcome the issue of varying link capacity, we apply MCSs to calculate the proper quantum size and the updated deficit counters for DRRF. In other words, we basically use the number of _requested slots_ not the number of bytes in the queue length (in downlink direction). The derivation is the ceiling of the queue length and its MCS size (in terms of number of bytes).  
For example, for downlink PUSC with 10 MHz and 1024 FFT (Fast Fourier Transform), the number of _subcarrier\(\times\)symbol_ combinations per slot is 56. Of these, 8 combinations are used as pilots leaving 48 combinations for data. With one bit per symbol coding, this results in 48 bits (or 6 bytes) per slot [1,5]. In case the queue length is 125 bytes, the number of requested slots is \(\lceil 125/6\rceil\) or 21 slots.  
For uplink PUSC, the requested slots can be derived from the bandwidth request. However, since there is no mechanism to send the information about individual packet sizes to BS, the concept of deficit round robin cannot be applied directly.  
For performance comparison, we also apply the MCS levels to calculate the fair share for GPS; and the proper quantum size and updated deficit counters for DRR. In other words, again _the resource allocation unit is in terms of number of requested slots_.  
Notice that in Section 2, if the fair share and the quantum size are measured in slots, in those particular examples, each slot's capacity is one byte. However, different MCSs result in a different number of bytes per slot, and thus result in different fair share and quantum sizes in bytes. For the rest of the paper, we use the number of requested slots instead of requested bytes for all three schedulers in order to achieve _throughput fairness_ for various channel conditions.
# Estimating BDS-3 Satellite Differential Code Biases with the Single-Frequency Uncombined PPP Model
## 4 Experimental Results and Analysis
### Satellite DCBs Estimated by SF Data from Multiple Stations
In summary, the implementation of scheme 2 has resulted in significant improvements in the accuracy of the estimated satellite DCBs compared to the performance in scheme 1. This multiple station approach has proven to be crucial in enhancing the precision and reliability of satellite DCBs estimation. By leveraging the cost-effectiveness of SF receivers, a denser ground receiver network is attainable, mitigating accuracy discrepancies in ionospheric modeling across various regions and, thereby, improving the comprehensive precision and reliability of satellite DCBs estimations. Embracing observations from a network of monitoring stations yields a more exhaustive and representative dataset, minimizing the impact of localized ionospheric effects and elevating the overall quality of the satellite DCB estimates. Figure 7: The daily average and RMS values of the absolute offsets in the estimated satellite DCBs throughout the experiment’s duration.
# An Improved Back-Projection Algorithm for GNSS-R BSAK Imaging Based on CPU and GPU Platform
## 4 Results
### Experiments
To obtain the top view image of the target area, the receiver should be installed at a higher position or on an airplane for data collection. The resulting image will contain more information about the target scene. Figure 10: Comparison between the optical image and the resultant radar image. Figure 9: Comparison between imaging results, (**a**) is the imaging results of CPU, (**b**) is the imaging results of GPU. To verify the image quality obtained, cross-sectional measurements were taken along the range and azimuth in the area around target No. 6 (the edge of the gymnasium), where the echoes have good signal strength and continuity. As shown in Figure 11, the actual range resolution of target No. 6 is approximately 19 m, which is close to the theoretical value. The theoretically predicted azimuth resolution is approximately 0.95 m, which is much smaller than the length of the building. However, we can compare the length of the target's measured values to its physical length. As shown in Figure 11, the total length of the target's measured values is about 38 m, which is consistent with the optical measurement result. In addition, Figure 11,b show the cross-sectional measurement results of the images generated using GPU and CPU to run BPA, respectively. The contours generated by these two methods perfectly match each other, which shows that BPA can be executed in GPU without loss, and the calculation time is greatly reduced compared with the operation in CPU. The proposed method effectively applies BPA to GNSS-R BSAR, and its feasibility has been verified.
## 5 Conclusions  
In this research, we employ a hybrid framework that combines the handcrafted bag-of-features (BoF) method for feature extraction with a fourteen-layer deep neural network (DNN). This combination optimizes and effectively categorizes customer feedback related to online products. The BoF, along with the DNN, enhances the features by adding invariance and distinctiveness to the feature selection and classification models. Extensive experimentation was conducted to determine the optimal cluster dimension in BoF and the hyperparameters in the DNN. The proposed model was tested using customer feedback data collected from the AliExpress e-commerce website and Amazon product listings. Specifically, we selected the InstaX3 camera listing pages from three different sellers on  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Dataset** & **Performance Metric** & **Second Name** & **DT** & **SVM** & **KNN** & **BiLSTM** & **LSTM** & **Proposed** \\ \hline \multirow{8}{*}{Amazon} & True positive rate (TPR) & Sensitivity & 83.80 & 84.63 & 83.76 & 89.44 & 89.83 & 90.21 \\  & True negative rate (TNR) & Specificity & 83.80 & 84.63 & 83.76 & 89.44 & 89.83 & 90.21 \\  & Positive predictive value (PPV) & Precision & 83.80 & 84.63 & 83.76 & 89.44 & 89.83 & 90.21 \\  & Negative predictive value (NPV) & - & 83.80 & 84.63 & 83.76 & 89.44 & 89.83 & 90.21 \\  & False positive rate (FPR) & Fallout & 16.02 & 13.56 & 14.27 & 09.56 & 11.38 & 07.89 \\  & False negative rate (FNR) & - & 16.02 & 13.56 & 14.27 & 09.56 & 11.38 & 07.89 \\  & False discovery rate FDR) & - & 16.02 & 13.56 & 14.27 & 09.56 & 11.38 & 07.89 \\  & Overall accuracy (ACC) & - & 83.49 & 83.14 & 83.69 & 87.80 & 87.66 & 90.42 \\ \hline \hline \end{tabular}
\end{table}
Table 1: _Cont._  
Figure 5: Performance metric comparison for various classification frameworks.  
both platforms. This dataset comprised a total of 3512 reviews, including 1802 positive and 1710 negative reviews from AliExpress, as well as 1552 reviews from Amazon, with 890 being positive and 662 negative. The classification model achieved a classification accuracy of 91.54% and 90.42% on the AliExpress and Amazon data, respectively. These results demonstrate the superior performance of the proposed model in categorizing customer feedback effectively.  
This research work was funded by the institutional Fund Projects under grant no. (IFPRP: 461-830-1443). The author gratefully acknowledges technical and financial support provided by Ministry of Education and King Abdul Aziz University, DSR, Jeddah, Saudi Arabia.  
The author declares no conflict of interest.