esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Conclusions  
This study offers several significant contributions. First, collaborative routing facilitates personalization by accounting for user heterogeneities, leading to increased privacy concerns. To address this issue, the proposed method combines MPC with collaborative routing, thereby enabling privacy-preserving collaboration and showcasing a potential solution to privacy concerns associated with personalization. Second, the study introduces a V2V-based position proof approach as an alternative to the widely used GPS, which has raised concerns regarding the sharing of privacy-sensitive information. This alternative allows users to verify their travel history without disclosing their historical positions, a characteristic that has not been achieved previously. Third, the study presents a novel on-chain/off-chain structure that capitalizes on the tamper-resistance property of on-chain data while maintaining sensitive privacy pattern information off-chain. This design offers valuable insights into harnessing the benefits of blockchain technology while circumventing privacy risks associated with its inherent transparency. It should be emphasized that the suggested approach extends significantly beyond the scope of [21], as the primary objective of the current study is to address potential privacy breaches arising from personalization within transportation systems. The MPC framework may be adapted to alternative application contexts involving personalized demand-side solutions. Furthermore, the position verification technique can be employed in additional applications necessitating the sharing of travel history, thereby safeguarding user privacy.  
Potential directions for future research include: (i) making position proofs reusable for witness vehicles to reduce duplication of verification; (ii) refining the MPC structure to allow more vehicles in one vehicle group; and (iii) incorporating the incentive mechanism into the broader intelligent transportation system to form a sustainable incentive ecosystem, where users can spend the incentives they gain, such that pseudonyms do not need to be connected to bank accounts, further protecting privacy.  
Conceptualization, C.W. and S.P.; methodology, C.W.; software, C.W.; validation, C.W.; formal analysis, C.W.; investigation, C.W. and S.P.; resources, S.P.; data curation, C.W.; writing--original draft preparation, C.W.; writing--review and editing, S.P.; visualization, C.W.; supervision, S.P.; project administration, S.P.; funding acquisition, S.P. All authors have read and agreed to the published version of the manuscript.  
This study is supported by the National Science Foundation (NSF) Smart and Connected Communities (SCC) grant #12125390, and by Georgia Institute of Technology. Any errors or omissions remain the sole responsibility of the authors.  
Not applicable.  
Data Availability Statement: Data are contained within the article.  
The authors declare no conflict of interest.
## 3 The Proposed Control System  
In order to improve the control performance of the MMC system, a novel control strategy is introduced that combines the VSG control with the capacitor voltage balancing CVB method. The VSG control is utilized to enhance dynamic response, improve system stability, and facilitate power sharing and inertia emulation while the CVB method is employed to redistribute the energy stored in the SMs capacitors, ensuring uniform voltage levels and equalizing the voltage across the capacitors. The proposed control design for the grid-connected MMC is shown in Figure 2.
# Low-Complexity GSM Detection Based on Maximum Ratio Combining
###### Abstract  
Generalized spatial modulation (GSM) technology is an extension of spatial modulation (SM) technology, and one of its main advantages is to further improve band efficiency. However, the multiple active antennas for transmission also brings the demodulation difficulties at the receiver. To solve the problem of high computational complexity of the optimal maximum likelihood (ML) detection, two sub-optimal detection algorithms are proposed through reducing the number of transmit antenna combinations (TACs) detected at the receiver. One is the maximum ratio combining detection algorithm based on repetitive sorting strategy, termed as (MRC-RS), which uses MRC repetitive sorting strategy to select the most likely TACs in detection. The other is the maximum ratio combining detection algorithm, which is based on the iterative idea of the orthogonal matching pursuit, termed the MRC-MP algorithm. The MRC-MP algorithm reduces the number of TACs through finite iterations to reduce the computational complexity. For M-QAM constellation, a hard-limited maximum likelihood (HLML) detection algorithm is introduced to calculate the modulation symbol. For the M-PSK constellation, a low-complexity maximum likelihood (LCML) algorithm is introduced to calculate the modulation symbol. The computational complexity of these two algorithms for calculating the modulation symbol are independent of modulation order. The simulation results show that for GSM systems with a large number of TACs, the proposed two algorithms not only achieve almost the same bit error rate (BER) performance as the ML algorithm, but also can greatly reduce the computational complexity.  
generalized spatial modulation (GSM); maximum likelihood (ML); maximum ratio combining (MRC); computational complexity; transmit antenna combination (TAC)
# Optimizing Support Locations in the Roof-Column Structural System
## 4 Numerical Analysis
### Result Validation  
To demonstrate the efficiency of the proposed method, optimization results from Figure 5 are compared with manual results in Figure 6, which were generated from user-defined column locations arranged in \"ring\" and random configurations, as shown in Figure 6a,b, respectively. The ring-configuration selection was due to the shape similarity to the optimization results possessing small compliance in Figure 5. Small-to-large rings were tested, and the purpose of utilizing manual ring configurations was to systemically examine whether the optimization solver had obtained a near-global optimal solution, as highlighted in Figure 5. Furthermore, the utilization of random configurations demonstrated that there were many other potential combinations of column locations, representing a tedious trial-and-error process.  
Compliance comparison across different methods is summarized in Figure 6c. First, it was confirmed that the optimization solver successfully obtained a near-global optimal solution using 15 \(\times\) 15 candidate columns, which had the smallest compliance across all tests. This suggests that a parametric study may be needed if the global optimal solution is part of the design requirements for future applications. Second, the compliance of random tests was much greater than that of the optimization results. The averaged compliance of random tests from Figure 6b was approximately 370 and 154 times larger than the optimization results from Figure 5b (\(t_{s}\) = 0.2) and Figure 5c (\(t_{s}\) = 0.3), respectively. This again confirmed the efficiency of the optimization method for automatically finding column locations and avoiding extremely large compliance.  
The proposed method is therefore effective in enabling the rapid design of column locations under a flat roof, which has high potential to be combined with complex architectural designs, including irregular roof shapes, discussed in the following section.  
Figure 5: Finding 8 optimal column locations under a square roof. (**a**) Three-dimensional illustration of candidate columns. Optimization results of \(t_{s}\) = (**b**) 0.2 and (**c**) 0.3.
## 2 Materials and Methods
### Data Recording and Processing  
The point of application of the ground reaction force (\(x_{\text{GR}}\)) was recorded during each trial with the use of a time-synchronized apparatus (BTS Bioengineering, Milano, Italy) constituted by two adjacent force plates (P-6000) [12], an eight-camera optoelectronic motion capture system (Smart-DX 7000) [13], and four reflective markers (6-mm diameter) which were attached on the medial and lateral malleoli, following a procedure illustrated in detail in a previous work [8]. The selected knee angle (\(\theta_{\text{knee}}=135^{\circ}\)) was reached using a twin axis SG150 electrogoniometer (Biometrics, Newport, UK) [14]. Data were recorded at a sampling frequency of 1 kHz. No filtering was applied to the kinematic and kinetic data. For each trail, the recorded values of \(x_{\text{GR}}\) were averaged over the duration of the trail, averaged again between the left and right foot, and normalized to foot length (\(l_{\text{foot}}\)), to get the value that was then used in the statistical analysis.
## 3 Algorithms and Implementation
### Sensor Estimator  
The estimators utilize the measurable physical quantities, i.e., current and terminals voltage, of the cell in order to estimate its internal state. In a real application, the measurement is achieved by an acquisition unit collected by voltage and current sensors as well as single or multiple analog-to-digital converters (ADC). This ADC converts the attained analog signals into digital values. Typically, this procedure introduces a measurement noise. The estimators must be able to execute a correct estimation even in the presence of these perturbations. In order to develop reliable algorithms and to test their capability to work in an actual system, a model of the sensors has been developed.  
This specified model uses the current \(I\) as input during the cell voltages \(V_{i}\) produced by the model of the battery and delivers as output. The measured current \(\hat{I}\) and voltages \(\hat{V}_{i}\). Both current and voltage sensors are modeled in the same way, as shown in Figures 2 and 3. They introduce measurement noise as well as offset errors. Note that the mean and variance of the noise that was injected into the system were computed upon \(\pm\)10%. The value of these errors is settable to simulate systems with different specifications. Furthermore, the acquisition system block can model the quantization error, i.e., the difference between the input signal value and its quantized value. This error depends on the number of bits of the ADC and its input range. These are editable parameters of the acquisition system. In particular, the current sensor input range is different from the voltage one. In fact, in this application, the voltage is a quantity included in the cell operating voltage range, which is always greater than zero. On the contrary, the current ADC has a large bipolar range.
## 3 Results and Discussion
### Antibacterial Activity of Nanoparticles of Garlic Extract on Tested Bacteria by Plate Count Method  
Results showing the total viable counts of all tested bacteria in colony-forming units per millimeter (CFU/mL) post interaction with the garlic extract and bacteria broth culture are illustrated in Figure 2. The results proved that all tested bacteria were challenged by the garlic extract during overnight incubation and led to a high reduction in cell number of _S. mutans_ at 100 mg/mL. _S. mutans_ decreased from 1.77 \(\times\) 10\({}^{4}\) CFU/mL (control) up to 0 CFU/mL, _P. gingivalis_ showed a decline from 2.01 \(\times\) 10\({}^{4}\) to 0 CFU/mL, _S. aureus_ sub. _aureus_ reduced from 2.05 \(\times\) 10\({}^{5}\) CFU/mL to 0 CFU/mL, and the most resistant bacteria _E. coli_, decreased to 0 CFU/mL when tested with 100 mg/mL concentrations, compared to the control (2.03 \(\times\) 10\({}^{5}\) CFU/mL).  
In general, there was a reduction in total viable counts of all tested bacteria when bacteria interacted with the different concentrations of garlic extract from the lowest concentration of 5 mg/mL to the highest concentration of 100 mg/mL compared with the control bacteria cultures. In this study, it was discovered that increasing the concentration of garlic extract reduces the number of bacteria that survive in CFU/mL; these findings are consistent with those of [PERSON] et al. [45], who compared the antibacterial effects of garlic and onion on _E. coli_, _Salmonella typhi_, _Pseudomonas aeruginosa_, and _Klebsiella pneumoniae_.
# Cyber-Physical-Social Frameworks for Urban Big Data Systems: A Survey
## 5 Data Processing
### Rule Formulation and Management  
A number of the reviewed works that involve the generation of rules and management of defined tasks involve semantic web-based technologies. The Wi-City-Plus [38] smart city system employs rules expressed in fuzzy logic to determine the minimum average customer satisfaction for an event or service (using social network data). The rules are implemented using SPARQL Protocol and RDF Query Language (SPARQL) queries. An ontology-based CPSS framework is proposed in this work, with data from both public and private databases of city interest, and data from participatory sensing, mapped to a defined city ontology. The ontology model consists of two parts: the urban environment model that describes concepts related to user behavior, urban infrastructures and city services and a user model ontology that represents a user's preferences and context. The environment monitoring data, in JSON format, is mapped to the RDF format according to the defined ontology. It is then available to be retrieved by SPARQL queries that can return values for instance, for temperature recorded by the closest weather station.  
Another ontology-based effort for a self-organizing resource network has been proposed in [11; 55]. The authors represent the physical, cyber and human space of a CPSS through sets of resources. Physical devices and humans are modeled as agents, which have structural (tasks to be solved by the agent) and parametric (situation knowledge, characterized by context) knowledge and a profile (including preferences, provided services and the related functionality). Ontology matching is employed to match the capabilities of the modeled agents with application-specific tasks, for example, to determine the resources (both sensors and actuators) that are in the specific states for a smart home cleaning scenario [11].  
Seldom do commercial systems use a specific semantic standard. Nest defines its own data models, and does not include any reference to standard ontologies. SmartThings started defining their own models too, but recently are moving towards utilizing Open Connectivity Foundation (OCF) [91], which aims at defining a standard model to represent devices and their capabilities tailored for the IoT. Other initiatives along these lines are the Web of Things Model [92], the purpose of which is to provide a standard semantic model to describe physical objects on the Web.
## 6 Complexity Analysis
Therefore, when comparing the proposed matching scheme for antenna selection for each RF chain at each AP to DSAS and CMDAS, the proposed matching strategy can yield a computational complexity reduction of around 200%. Finally, in uplink cell-free mm-Wave massive MIMO systems, there is a tradeoff between EE, SE, and the number of FLOPs used as a complexity analysis in this paper. Figure 10 illustrates this tradeoff. The solid curves in this figure show the pro  
Figure 9: Complexity analysis based on floating-points operations (FLOPs) with different scenarios of uplink cell-free mm-Wave massive MIMO systems. **(a)** FLOPs versus \(M\) APs for a system with \(N_{r}=48\), \(K=8\), \(\rho=23\) dBm, and \(N_{Q}=8\) CPSs. **(b)** FLOPs versus \(N_{r}\) antennas with \(M=80\), \(K=8\), \(\rho=23\) dBm, and \(N_{Q}=8\) CPSs. **(c)** FLOPs versus the number of \(K\) UEs with, \(M=80\), \(N_{r}=48\), \(\rho=23\) dBm, and \(N_{Q}=8\) CPSs. posed matching scheme's energy efficiency, spectral efficiency, and FLOPs tradeoff when \(M=\{16,32,48,64,80\}\), \(K=8\), \(\rho=23\) dBm, \(N_{Q}=8\) CPSs, and \(N_{r}=48\). In addition, the dashed curves demonstrate the EE, SE, and the FLOPs tradeoff when \(N_{r}=80\). The EE decreases when both \(M\) APs and \(N_{r}\) increase while the SE increases. The FLOPs present a reverse trend when compared with the EE and SE tradeoff, i.e., FLOPs increase when both \(M\) APs and \(N_{r}\) increase. Analysis of the simulation results provides clear insight into antenna selection techniques for uplink cell-free mm-Wave massive MIMO systems when different scenarios of \(M\) APs, \(K\) UEs, and \(N_{r}\) are considered. Overall, the results confirm that the proposed matching strategy based on the Hungarian method can provide better EE and lower complexity compared to the other schemes. Moreover, the proposed matching scheme for antenna selection can achieve a tradeoff between SE, EE, total power consumption, and computational complexity.
# The Design and Deployment of an End-To-End IoT Infrastructure for the Natural Environment
## 5 Deployment Experiences of an Environmental IoT
### Design of the Animal Tracking Node  
This node performed well and was unaffected by rain issues, though some build-up of condensation inside the node housing was observed, likely a particular problem as these nodes were in prolonged contact with warm animals and an unusually cold and wet environment. Reports from the local farmers indicated that the sheep to which the nodes were attached appeared behaviourally unaffected by the deployment.
## 6 Discussion  
More recently, we have witnessed the potential of deep learning methods for AD progression prediction. [PERSON] et al. [3] used RNNs with LSTM cells for predicting future cognitive scores, while [PERSON] et al. and [PERSON] et al. [4,5] employed RNN variants for AD diagnosis. However, these studies focused on individual prediction tasks and did not consider the correlations among different tasks. In contrast, [PERSON] et al. and [PERSON] et al. [7,13] demonstrated the advantages of the simultaneous learning of multiple tasks, which resulted in improved prediction accuracy.  
Borrowing the idea of multi-task learning, our proposed model performs AD clinical diagnosis and cognitive score predictions simultaneously. To address the unresolved issue of selecting the appropriate loss weight for each individual task in the methods of [PERSON] et al. and [PERSON] et al. [7,13], we introduce the DWA scheme. This scheme automatically adjusts each task's weight, enhancing the overall model performance. Furthermore, unlike previous methods for AD progression prediction that focused solely on enhancing the accuracy and ignored the measurement of result reliability, our model introduces the theory of evidential deep learning to provide uncertainty estimates for the model's predictions. By utilizing this uncertainty awareness mechanism, our model can help foster trust in deep learning methods among clinicians.  
Despite achieving promising results, our model also has some limitations that require addressing in the future. Firstly, while the predictive performance of our model is notable, there is still room for improvement. To enhance prediction accuracy further, incorporating the attention mechanism into the model could prove beneficial. Secondly, utilizing only the clinical diagnosis and 22 continuous variables as features is insufficient to fully characterize the course of AD. There likely exist supplementary features that could aid in detecting and discerning subtle changes in a patient's disease progression. Consequently, future research efforts should pay attention to better extracting AD features from the dataset, thereby facilitating a deeper comprehension of the disease's evolution and enhancing the model's predictive capacities.
## 4 Data Storage and Processing: Automated Data Processing and Storage
### Scalable and Autonomous Deployment Using Containerization Strategies  
Scalable deployment is a critical aspect of cloud-based data processing in automated irrigation systems. Containerization, a method of packaging software code and all its dependencies so that it can run uniformly and consistently on any infrastructure [87], plays a crucial role in this process. Technologies like Docker and Kubernetes facilitate the efficient deployment and scaling of data processing modules in irrigation management systems.  
In the context of automated irrigation, containerization allows for the encapsulation of various software components such as data processing algorithms, irrigation scheduling modules, and weather prediction models into separate, portable units. For example, [PERSON] et al. [88] demonstrated the use of docker containers in a smart irrigation system, where each container housed specific software functionalities like data analysis and control mechanisms. This approach enables easy updates and modifications to individual components without affecting the entire system. Figure 4 depicts such a containerization management system.  
The scalable process in containerized irrigation systems involves the following:  
1. packaging irrigation algorithms and data processing modules into containers;
2. deploying these containers across multiple servers or cloud instances;
3. automatically scaling the number of container instances based on data volume or processing needs;
4. ensuring consistent performance across different environments, from edge computing devices to cloud servers.  
Figure 4: Containerized irrigation management system. Just as shipping containers standardize cargo transport, software containers can package irrigation system’s software components. Each container (color-coded) encapsulates a specific function, allowing for efficient deployment and scalability across the system, orchestrated by systems like Kubernetes.
# Vision-Language Models for Zero-Shot Classification of Remote Sensing Images
## 5 Conclusions  
In this paper, we have presented an approach to address the challenging task of zero-shot classification in RS. Previous solutions struggled to achieve satisfactory results when attempting to classify unseen classes using image-based models. In this study, we have introduced a novel strategy based on VLMs that have been pre-trained to understand the relationships between images and text. Specifically, we have explored thirteen VLMs derived from CLIP/Open-CLIP, each with varying parameter complexities. Through a series of experiments, we have identified that the prompt \"a satellite image of + class_name\" is the most effective prompt for RS images to query the language backbone of the CLIP model. The obtained results exhibit better compared to existing RS solutions on three well-known RS scene datasets. This research indicates a promising direction for improving zero-shot classification in the field of RS, leveraging the power of VLMs to enhance classification accuracy for unseen classes.  
For future research, we propose focusing on fine-tuning VLMs, exploring different model architectures, and addressing challenges specific to temporal and multimodal data in addition to images with coarser resolution.  
Methodology, M.M.A.R. and Y.B.; Software, Y.B.; Formal analysis, H.E.; Investigation, H.E.; Writing--original draft, M.Z.; Writing--review & editing, M.M.A.R. All authors have read and agreed to the published version of the manuscript.  
Researchers Supporting Project number (RSPD-2023R607), King Saud University, Riyadh, Saudi Arabia.  
Not applicable.  
Available in Merced [17], NWPU-RESISC45 [18], and AID [19].  
This research was supported by the Researchers Supporting Project (RSPD-2023R607), King Saud University, Riyadh, Saudi Arabia.  
There is no conflict of interest.