esa-sceva/satcom-chunk-collection huggingface dataset:
# Additive Manufacturing of Variable Density Lenses for Radio Frequency Communications in X-Band
## 3 Horn Antenna and Gradient Index Lens Design  
The objective of the design was to obtain a complete set with a horn antenna and GRIN lens, which meet certain EM and mechanical specifications. The main idea behind the GRIN lens is that it focuses the radiation pattern of a radiating element, enhancing the directivity of the set. In this general approach, two main elements were designed, on the one hand, the radiating element, and on the other hand, the lens. The electromagnetic design is frequency-dependant, but scalable, allowing a design developed for one frequency to be easily adjusted for another. The selected working frequency for the set was 10 GHz, covering services in X-band satellite communications. On the other hand, mechanical issues were considered, such as the weight of the set and how the radiation element and the lens are built up together, in order to obtain a compact element ready to be used.
# Progress in Energy Storage Technologies and Methods for Renewable Energy Systems Application
## 3 The Progress of Energy Storage Technologies
### Superconducting Magnetic Energy Storage  
SMES unit is a device that stores energy in the magnetic field generated by a direct current flowing through a superconducting coil [2]. A typical SMES system consists of three main components, including a superconducting coil unit, a power conditioning subsystem, and a refrigeration and vacuum subsystem. During the discharge phase, the SMES system can release the stored electrical energy back into the alternating current (AC) system through the connected power converter module. The amount of stored energy is determined by the self-inductance of the coil and the current flowing through it. Low-temperature superconducting (LTS) coils with an operating temperature of 5 K and high-temperature superconducting (HTS) coils with an operating temperature of 70 K. Compared to rechargeable batteries, SMES devices are capable of discharging with near full stored energy after thousands of complete cycles with minimal degradation. Popular research lies in the use of high-temperature superconductivity to generate DC electric fieldsfor field-induced superconductivity [52; 53] and the ability of superconductivity to confine atomic sequences [54; 55].
# Impact of Dataset Size on Classification Performance:
## 2 Methodology
### Dataset  
We selected twenty data sets from the UCI data repository [24]. The datasets were selected from medical fields where limited data are common. Table 2 shows details about the selected datasets, arranged by size, along with their number of attributes and data type. There is no explicit definition for small datasets in the literature. Therefore, in order to determine the size range for selecting small datasets in this work, we reviewed existing works that study small datasets and kept track of the size of their datasets. As shown in Table 1, the size of small datasets used in the existing works ranges from 18 to 1030 across studies [8, 11, 12, 13, 14, 15, 17, 18]. Accordingly, the selected twenty datasets were categorized as eighteen small datasets and two large datasets. The small datasets (DS1-DS18) consist of eighteen medical datasets. The number of instances in these small datasets ranges from 80-1040 instances, and the number of features ranges between 3-49. All small datasets are numerical or numerical with text. In the category of large datasets, there are two datasets; Skin Segmentation dataset (DS19 in Table 2) and Diabetes 130-US hospitals dataset (DS20 in Table 2). The former consists of 245,057 instances and four features of numeric datatype, while the latter has 9871 instances and 55 features of mixed numeric and text datatypes. To study the impact of dataset size on the performance of classifiers, we constructed three small sub-datasets of increasing sizes from each large dataset using sampling without replacement, as shown in Table 3. Figure 1 presents the dataset portioning algorithm. As shown in the figure, the algorithm receives two large datasets S\({}_{1}\) and S\({}_{2}\) and returns three small sub-datasets S\({}_{1}\), S\({}_{2}\), and S\({}_{3}\) for each large dataset. It first defines the sizes of the three small sub-datasets (980, 490, and 98). These were selected from the three equal intervals (highest, middle, and lowest) of the size range of small datasets (18-1030), respectively. Next, the algorithm iterates over the large datasets S\({}_{1}\) and S\({}_{2}\). For each dataset, the algorithm creates a copy of the dataset (SL) to void modifying the original dataset. The algorithm then iterates over the array of small sizes in order to create the corresponding small sub-dataset \(\text{SS}_{i}\), where X tuples are extracted randomly without replacement to avoid overlapping between the sub-datasets. This is performed by removing the sub-dataset \(\text{SS}_{i}\) from the large dataset SL after extraction. The iterations continue until all three sub-datasets are created for all large datasets. Data preprocessing was carried out for all datasets as necessary to deal with missing values.
# Analysis of Instantaneous Energy Consumption and Recuperation Based on Measurements from SORT Runs
###### Abstract  
Using the standardised SORT, the article analyses instantaneous energy consumption and recuperation processes in an electric bus. The test includes three scenarios: SORT 1 (heavy urban traffic), SORT 2 (mixed driving conditions), and SORT 3 (suburban routes), enabling precise assessment of the energy efficiency of vehicles while eliminating environmental variables. The recuperation system significantly enhances energy efficiency, though its effectiveness varies based on the driving scenario. Modelling methods were compared as follows: linear regression, KNN algorithms, and neural networks, achieving a high fit (R\({}^{2}\) > 90%). While KNN and neural networks were better at reproducing nonlinearities, they indicated the need for additional variables and time delays to enhance accuracy. The article sets itself apart by incorporating predictive models and examining recuperation efficiency across various scenarios. It emphasizes the importance of combining SORT results with real operational data and developing adaptive energy management systems. The results indicate the potential for optimizing electric buses for public transport, including route planning and further improving recuperation technology, which can significantly reduce energy consumption and greenhouse gas emissions.  
e 2025 15 1681 +
Footnote †: journal: Applied sciences  
Article
# Priority-Driven Resource Allocation with Reuse for Platooning in 5G Vehicular Network
## 4 System Modeling and Resource Allocation Algorithm
### Proposed Resource Allocation Algorithm
Through this comparative evaluation, the proposed algorithm can be objectively assessed in terms of its efficiency in handling real-time resource allocation within the complexity of solving the resource allocation problem and the degree of performance improvement it offers compared with existing algorithms. The proposed algorithm comprises two main steps. The first step involves the base station classifying vehicles as either platoon vehicles or general vehicles during the resource request stage, while the second step involves the base station allocating resources to both platoon and general vehicles during the resource allocation stage. The algorithm follows a heuristic-based greedy approach, where the base station determines whether to reuse resources for each vehicle based on the SINR conditions and the resource reuse distance, enabling efficient resource allocation in real-time vehicular networks. The detailed operational process of the algorithm is illustrated in the flowchart shown in Figure 7 and further explained in Algorithms 1-3. In Algorithm 1, during the resource allocation process, vehicles are classified as platoon vehicles or general vehicles, followed by an initialization step. The base station first receives the identifier of each vehicle from the platoon vehicles and stores it in the identifier set \(V_{p}\). In the resource allocation stage, the base station identifies the vehicles requesting resources and verifies whether their identifiers are included in \(V_{p}\). Vehicles whose identifiers are part of \(V_{p}\) are classified as platoon vehicles and stored in set \(P\), while general vehicles are placed in set \(G\). Subsequently, general vehicles in set \(G\) that have not yet received resources are prioritized based on the number of resource allocation failures, denoted as Score, and resources are allocated first to the platoon vehicles. Algorithm 2 addresses the resource allocation process for platoon vehicles. Resource allocation is performed in the order in which vehicles have submitted requests, with the base station identifying a resource block for each vehicle. Initially, the base station searches for available resources for the requesting vehicle. If the resource is unallocated, meaning it is not currently in use by another vehicle, it is immediately allocated to the requesting vehicle. If the resource is already in use by another platoon vehicle, the base station determines  
Figure 7: Flowchart of the algorithm. whether the resource can be reused by the requesting vehicle. In this case, the base station calculates the SINR for all platoon vehicles using the resource and allows reuse only if the SINR of all vehicles is greater than or equal to the threshold SINR.
## 5 Results and Discussion  
In this section, the performance of the proposed scheme over an IM/DD optical channel is investigated and evaluated in terms of its PAPR, power spectral density (PSD), achievable transmission rate, BER and complexity. The POF transmission setup is depicted in Figure 4. Ten-bit DAC and eight-bit ADC are both adopted, resulting in negligible quantization noise. A standard 50-m Polymethyl Metacrylate (PMMA) step index (SI) POF (150 dB/km @650 nm) is employed. The DMT signals are pre-generated by a computer and stored in the arbitrary waveform generator (AWG) memory. The analogue electrical signal of the AWG output is then used to drive a 650-nm RC-LED with a 10-mA bias current. After the SI-POF transmission, the received optical signal is detected by the PD and then captured by the ADC components. Finally, digital signal processing is implemented in MATLAB. With respect to the received probing symbols, the estimated channel information is obtained and the normalized noise PSD is measured at approximately \(-110\) dB/Hz, which will be used in the following simulations. The DMT parameters are depicted in Table 1.  
Figure 4: Transmission setup with offline processing.  
Figure 3: Diagram of proposed decoding at receiver.
# CDSTAEP: Cross-Domain Spatial-Temporal Association Learning for Abnormal Events Prediction
## 5 Experiments and Evaluation
### Discussion  
Limitations of the CDSTAEP model. First, because the CDSTAEP model relies on the representation learning results of historical log when deployed online, it will face problems such as changes in the network structure and new events, resulting in repeated training of the model. Second, CDSTAEP incorporates spatial relationships between domains, entities, and events, as well as temporal relationships between events, but faces difficulties in effectively fusing behaviors, more complex attributes of events (e.g., threat level, threat type). Integrating more information can predict events in a more targeted manner and more effectively improve the possibility of predicting unknown abnormal events.  
Future work. Future work will focus on how to represent dynamic, multi-dimensional, and complex attribute information in the Riemannian space, effectively integrate more dimensional attribute vectors, improve the prediction effect of abnormal network events, and provide more effective countermeasures for active network defense.
# Optimizing Time-Sensitive Traffic Scheduling in Low-Earth-Orbit Satellite Networks
## 2 System Model  
In this section, we first introduce the satellite-terrestrial network architecture and then discuss the queuing and forwarding mechanism for data flows.
# From Feed to Fish--Nutrients' Fate in Aquaculture Systems
## 3 Results
### Fislpond and Cultivation Characteristics  
In four of the seventy-four assessed papers, the authors referred to the fish production they studied as being 'extensive'; in nine papers, the production was'semi-intensive'; and in seven papers, the production was considered to be intensive, while fifty-four papers did not report the type of production (Table S2). In extensively managed fishponds, average fish yields ranged from 38 to 584 kg ha\({}^{-1}\); in semi-intensive production, yields ranged from 755 to 13,312 kg ha\({}^{-1}\); and in intensive production, yields ranged from 53 to 43,358 kg ha\({}^{-1}\). The lowest value from intensive production was due to the low cultivation period (73 days).  
The 74 papers collectively recorded a total of forty-seven fish species and two species of prawn cultivated together with fish. Of the 47 fish species, only 14 were studied in more than two papers. These species were cultivated in polyculture, monoculture, and integrated systems (Figure 4). Differently from monoculture and polyculture, in which the main produced species are fish, the integrated systems combine different activities--e.g., agriculture and fish farming--where the output of one component is utilized as input for the other to maximize productivity and reduce environmental impacts.  
Examples of integrated systems in our database were agriculture-aquaculture (IAA) that included farming of rice and fish [12], agricultural residues applied to ponds and pond water used for irrigation [13], and soybean cultivation within the dry pond and later used in feed formulation [14]. Integrated cultivation systems were also reported as integrated multi-trophic aquaculture (IMTA), which combines fed species (commonly finfish) with an extractive species (e.g., filter-feeders such as fish or prawns) reared in the same pond or in enclosures (cage-culture) [15] and in-pond raceways [16]  
The species _Oreochromis niloticus_ and _Cyprinus carpio_ were the most cultivated species, reported in 30 and 19 papers, respectively, and were cultivated in monoculture, polyculture, and integrated cultivation systems. In IMTA, the fish species _Catla calla, Labeo rohita, Cirrhinus nurgala_[17; 18], _Mugil cephalus, Creuopharyngealto della, Hypophthalmichthys molitrix_[19], and _Oreochromis niloticus_[20] were cultivated with the prawn species _Macrobachium vasbergi_. The prawn species _Macrobachium amazonicum_, in turn, was only reported in Brazilian aquaculture and was cultivated in _Oreochromis niloticus_[21; 22; 23] and _Colosoma macropomum_ ponds [24; 25; 26].  
Figure 3: Assessed papers’ locations across the world. Dots indicate the studies’ locations, and triangles indicate the papers focusing on nutrient budget location.
# How Different Load Carriage Scenarios Impact the Gait in Police Officers
## 1 Introduction  
Special police forces are required to carry heavy loads as part of their daily routines [1, 2]. Although such loads may have protective effects with regard to specific tasks [1, 3], evidence has shown that the load used often exceeds the maximum of 45% body mass [4]. Moreover, the magnitude of absolute load being carried is rapidly increasing [1, 3]. This negative trend affects human locomotion, increasing the energy expenditure costs [1, 5] and the level of musculoskeletal injuries [2, 6]. Indeed, previous work has demonstrated that the most common injuries are related to stress fractures and lower body joints, mainly the hip, knee, ankle, and foot [2].  
Carrying high loads can impact the biomechanics of gait. In a recent review looking at the impact of load carriage on military personnel [7], load carriage had no effect on spatiotemporal variables, like step or stride length, cadence, or step width [8, 9, 10, 11], yet the increases in vertical peak forces and pressures in loaded conditions, compared to unloaded conditions, were consistently reported [8, 12, 13]. It has been hypothesized that military personnel are more experienced in carrying heavy loads and their gait is too robust to be affected by additional loads [14]. On the other hand, additional added mass of the carried load gradually increases the ground reaction forces [13] and plantar pressures [9, 12], leading to greater breaking and propulsive forces [8, 10, 13].  
Special police officers often perform physically demanding tasks such as issuing arrest warrants, rural operations, and personal protection, and antiterrorism responses [15]. In addition, special police officers are challenged by performing vigorous physical activities, like running, pulling and pushing, lifting, carrying heavy objects, and negotiating obstaclesdaily [16]. All these activities are regularly accompanied by even heavier load carriage exceeding \(>\)50% of body mass, compared to military personnel [15; 17], leading to the conclusion that they walk less and have lower step frequency [18]. Due to biomechanical adaptations under different loading conditions, previous findings based on military personnel may not be applicable to special police officers [7].  
Therefore, the main purpose of the study was to examine the effects of load carriage on the spatiotemporal and kinetic gait changes in special police officers. Based on previous findings in military personnel [7], we hypothesized that heavier load carried by special police officers would have no significant effect on spatiotemporal gait changes, yet ground reaction forces and plantar pressures under the different foot regions would steadily increase.
# A Malware Detection Framework Based on Semantic Information of Behavioral Features
## 4 Dataset and Evaluation Methods
### Experimental Results  
#### 4.3.1 Comparison with Baselines  
We assess the performance of three traditional Machine Learning algorithms, which are Naive Bayes (NB), K-Nearest Neighbors (KNN), and Decision Tree (DT), and three Deep Learning models, which include BiLSTM, Text-CNN, and BiGRU. All algorithms measure results using accuracy, precision, recall, and F1-Score metrics.  
As shown in Table 4, the performance of deep learning models is generally superior to that of the machine learning model, indicating that the features of API sequences cannot be fully exploited based on frequency features alone. The accuracy of the model proposed in this paper, namely CNNs-BiGRU, is 0.9828, precision is 0.9870, recall is 0.9789, and F1-Score is 0.9827 on the test dataset, which is the best performance among all models of machine learning and deep learning. This indicates multiple CNN networks can extract local features at different scales, local and higher-order features of the call can be better captured, and BiGRU models can use contextual information more effectively to increase the detection performance on the model.  
Figure 8 plots the ROC curves [55] of various approaches over the test dataset, which represents the false positive rate (FPR) on the x-axis and the true positive rate (TPR) on the y-axis. Area under ROC curve is defined as AUC; the larger its value, the better the classifier works. It can be observed that our model obtains the best AUC score among all the baseline models of the test dataset, and the proposed model obtains a high TPR even with a low FPR.
# A Signaling-Free Underwater Code Division Multiple Access Scheme
###### Abstract  
In this paper, we propose an underwater code division multiple access system where each sensor node independently evaluates whether a channel is available or not without control message exchanges with a central data-gathering node named a sink. A sensor node is able to estimate how large power is currently received at a sink in the distance based on the overheard power at the node from neighbors. If the estimated power is below a certain threshold level, the sensor node is allowed to transmit data in a p-persistent manner, where the probability p depends on the available capacity. Simulation results show the traffic estimation works well as demonstrated by a success probability of approximately 100%, and the data throughput improves in most of the offered traffic region because of the removal of the control signaling related to channel allocation.  
underwater acoustic sensor networks; code division multiple access; multiple access control 17 June 2019; Accepted: 6 August 2019; Published: 8 August 2019
## 3 Performance Metrics and Application Scenarios in PLS
### Secrecy Outage Probability (SOP)
For example, when a source Alice only has the CSI of the legitimate channel and not the CSI of the wiretap channel, the allocation of power can be executed according to the information of the CSI of the legitimate channel. Thus, it is possible to minimize the SOP by carefully modeling the achievable rate of the secrecy capacity \(R_{S}\), the secrecy capacity of the legitimate channel \(R_{B}\) and the conditions for secure transmission [83]. In summary, the secrecy outage probability (SOP) is used in cases where the source transmitter has very little information about the CSI of the intended receiver or the eavesdropper. The SOP is more applicable in conditions where the statistical CSI of a wiretap channel is known to the source Alice. In general, the secrecy outage capacity/probability is used to measure the reliability of secure transmission strategies.