esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Hierarchical Cache Management (HCM) Policy
### SSD Cache Replica Management  
The write frequency of replicas in SSDs is much lower than that in DRAM, which allows for more refined replica management with relatively low overhead. To achieve this, we have designed a multi-queue cache replacement policy based on cache utility, where the cache utility can be customized by network administrators according to different cache optimization objectives. The multi-queue design allows replicas with different utility values to be provided with different levels of cache replacement frequency, thereby improving the overall cache performance. Specifically, replicas cached in the SSD are managed using two LRU queues, denoted as \(Q_{s-H}\) and \(Q_{s-L}\). \(Q_{s-H}\) is used to maintain replicas with high cache utility values, while \(Q_{s-L}\) is used to maintain those with low cache utility values. The two LRU queues are linked end-to-end, so that replicas replaced in \(Q_{s-H}\) can be directly demoted to \(Q_{s-L}\) for continued caching, thereby extending the residency time of high-utility replicas in the cache. Replicas replaced in \(Q_{s-L}\) are simply removed from the SSD cache. Before a new replica is written to the SSD cache, a classifier determines the target queue for insertion. Specifically, the classifier is responsible for maintaining a cache utility threshold (denoted as \(T_{utility}\)) and dynamically updating this threshold during the cache management process. For each replica to be written to the SSD cache, its corresponding utility value is calculated using a predefined cache utility function. Based on whether its utility value exceeds \(T_{utility}\), the replica is inserted into the corresponding LRU queue. Each new replica insertion triggers an update to \(T_{utility}\) to ensure it always reflects the real-time status of the SSD cache. The cache utility function can be customized according to different cache optimization goals, such as the replica's popularity, hop count, network traffic cost, etc. It should be noted that the utility threshold should be adjusted based on the utility function, ensuring that the number of replicas written to each queue aligns with the design objectives. Algorithm 2 provides a simple implementation, where the threshold is set as the mean of the maximum and minimum utility. This results in significantly fewer replicas being written to \(Q_{s-H}\) compared to \(Q_{s-H}\). As a result, \(Q_{s-H}\) experiences a lower replacement rate, which allows high-utility replicas to stay in the cache for a longer period. When a new request hits the SSD cache, the corresponding replica is first moved to the tail of the LRU queue it resides in, and its cache utility value is updated. If the replica is in \(Q_{s-L}\), it is further checked whether its updated utility value exceeds \(T_{utility}\). If so, the replica is promoted to \(Q_{s-H}\). Similarly, the update of each replica's utility value also triggers an update to \(T_{utility}\). An example of the update rule for \(T_{utility}\) is presented in Algorithm 2, while Algorithm 3 summarizes the SSD cache replica management algorithm described above.
# The Use of Construction Waste to Remediate a Thermally Active Spoil Heap
## 3 Results and Discussion
### Sequential Extraction Analysis  
The mobility of elements is related to sorption processes on clay minerals, humus and iron, manganese and aluminium oxides. It also depends on the hydrothermal conditions, such as the temperature and humidity of the given locality [44]. Most authors [45; 46; 47; 48] state that the uptake of metals by plants does not show a linear dependence on their total content in the environment, but above all on their availability. In order to understand the possible consequences for the environment, it is also necessary to know the form in which the metal is present in the environment. Since it is difficult to determine their exact form, methods that can determine to which fraction of the material the metal is bound are applied. It is a good compromise which can provide information about the risk of contamination [49; 50; 51; 52; 53; 24; 54; 55; 25; 26; 27; 28; 29; 30; 31; 32; 33; 34; 35; 36; 37; 38; 39; 40; 41; 42; 43; 44; 45; 46; 47; 48; 49; 50; 51; 52; 53].  
Sequential extraction analysis (SEA) essentially simulates processes that normally occur in nature, such as acidification, reduction (e.g., in the subsoil), or oxidation (e.g., in the topsoil). Various extraction agents with increasing extraction strength are used for this purpose, in which solid matrices are leached. Although these processes do not inform about the release kinetics of potentially toxic elements (PTEs) from the solid matrix during natural processes, they can provide information about their total amount that is potentially available under specific environmental conditions [29; 31; 54; 55; 56; 57].  
A simplified three-step methodology established by the European Commission as part of the Standards, Measurement and Testing Program (BCR analysis) was applied in order to determine the bioavailability of metals present in tailings and their mixtures with CDW, which could be applied either with soil admixture or even separately to modify the terrain in the mountainous landscape. BCR analysis includes three steps, and therefore three basic fractions, into which metals are divided according to the form in which they are found. Based on the analysis of the overall composition of tailings, it has been found that it contains a higher amount of aluminium and iron. That is why the BCR analysis was also focused on these two metals.
## 3 Results
### Quorum-Sensing Signaling  
All extracts were added into microplate wells containing Zobell medium seeded with a _V. harveyi_ culture aliquot. _V. harveyi_ growth and quorum-sensing regulated bioluminescence were monitored over 24 h (Figure 11).  
Figure 11: Effect of extracts on _V. harveyi_ growth and luminescence. Results were registered at maximum luminescence (12 h growth). Vh: _V. harveyi_ without the addition of the extract (reference). Sm: Streptomycin at 50 \(\mu\)g/mL. Blue stars: the significant effect on luminescence at \(p\)-value \(<\) 0.01. Red stars: the significant effect on growth (OD 600 nm) at \(p\)-value \(<\) 0.01. Assays were performed four times and were statistically compared to 92 assays with _V. harveyi_ alone using Student’s test.  
Figure 10: Antimicrobial activities of extracts against 13 indicator strains. 0: no activity; 1: medium activity; 2: high activity.  
None of the extracts influenced _V. harveyi_ growth but its luminescence was decreased by the NaOH extracts of _V. diabolicus_ (AB and AP) and by the NaOH extract of the MS969 biofilm (AB). Interestingly, the amount of light produced was increased by the biofilm extracts of MS969 (SnB and TOTB) suggesting that the MS969 strain released quorum-sensing (QS) signal molecules in the extracellular matrix.  
Quorum-sensing signaling is an important regulatory pathway in _Vibrio_ species and has been well-studied in _A. fischeri_[79] and _V. harveyi_[80]. Bioluminescence in _V. harveyi_ is regulated by the N-acyl homoserine lactone-based system, as well as two other systems, including an autoinducer AI-2 [81]. In the MS969 strain, biofilm-specifically released compounds have been shown to promote bioluminescence. This suggests that a quorum-sensing pathway similar to one of the three _V. harveyi_ QS systems was involved in biofilm formation of the MS969 strain. The _V. diabolicus_ biofilm may be regulated by signal molecules not recognized by _V. harveyi_ to produce bioluminescence, or most probably, they were not enough to be effective.
# Computer Vision and Machine Learning-Based Predictive Analysis for Urban Agricultural Systems
## 4 Model Development and Training
### Data Pre-Analysis  
Given that the gathered thermal data had a time series characteristic (each thermal image was gathered every 15 min) similarly to other sensor measurements, it was decided to investigate potential relationships between model input (thermal images) and output (temperature, humidity, soil water content, light intensity sensor) data. To analyze this relation, it was decided that before the model development, a data correlation analysis needs to be performed. This analysis evaluated how each pixel in all the thermal images correlated with readings from the other sensors (light sensor, temperature and humidity sensor, and soil sensor) over the two-month data collection period. This step was to understand the relationships between the surface temperatures of the plants, as captured by the thermal camera, and the environmental conditions measured by the other nearby sensors. It helped in identifying patterns and dependencies that could be useful when choosing the model input parameters and training the model. The correlation analysis results (Figure 4) confirmed that thermal images correlate well with nearby air temperature sensors, which was used as a reference. Figure 4 presents a series of correlation heatmaps, showing the relationship between pixel values over a 2-month period and various sensor readings. The axes of each heatmap correspond to the coordinates of individual pixels within a 120 \(\times\) 160 array, representing the image size.  
For instance, in Figure 4a, the pixel located at vertical pixel location 0 and horizontal pixel location 0 (X = 0; Y = 0) appears within a darker region, indicating a relatively high correlation with the air temperature sensor, as compared to other pixels in the image. This is supported by a correlation coefficient exceeding 0.5, as can be seen in the adjacent scale. It is important to note that these heatmaps contain a correlation analysis over a two-month period, comparing pixel values against the corresponding time series data of air temperature, relative humidity, soil water content, and light intensity recorded concurrently.  
It was determined that the light intensity correlated the least with thermal image data, although nearby surfaces, such as sensor casings, gave an indication that there is some amount of correlation visible.
# A Rectangular Notch-Band UWB Antenna with Controllable Notched Bandwidth and Centre Frequency
## 2 Antenna Design  
### Antenna Structure  
The geometry of the proposed antenna is shown in Figure 2. The antenna consists of a modified rectangular patch and two EBG structures. The lower corners of the patch are truncated by a semicircle with radius \(R\) to achieve UWB characteristics. The EBG structures consist of two rectangular conductors that are connected to the radiator using two shorting pins with radius \(p\). The EBGs are usually made up of small metal patches on dielectric substrates with shorting vias and have the potential to create a stopband to block electromagnetic waves of a certain frequency [25]. The motive of using EBG structure is the ease of design, and simplification of the antenna. The antenna is printed on Taconic TLY-5A substrate (\(\epsilon_{\mathrm{r}}\) = 2.17 and \(\tan\delta\) = 0.0009) with length \(L\) and width \(A\). The antenna is fed with a coplanar waveguide with a characteristic impedance of 50 \(\Omega\). The optimized dimensions of the antenna are as follows: \(A\) = 16 mm, \(L\) = 25 mm, \(h\) = 1.52 mm, \(Lg\) = 7 mm, \(Lp\) = 12 mm, \(r\) = 0.3 mm, \(Sw\) = 3.2 mm, \(Wd\) = 0.6 mm, \(l_{1}\) = 16 mm, \(l_{2}\) = 16 mm, \(w_{1}\) = 0.9 mm, \(w_{2}\) = 2 mm, \(s\) = 1.1 mm, \(g\) = 1 mm, and \(p\) = 0.2 mm.
# Dynamic Investigation of a Solar-Driven Brayton Cycle with Supercritical CO2
## 1 Introduction
The radiation-to-heat conversion efficiency for the proposed design of the central receiver was found to be 90%. Additionally, some other studies focused on the calculations for the HTF. [PERSON] et al. [23] have presented a methodology for calculating the thermal losses of the HTF. Furthermore, in the literature, there are a lot of studies presenting calculations for the overall layout of an STP. [PERSON] et al. [14] have used the particle swarm optimization technique to design and optimize an STP. Although the solar-to-electricity efficiency was found to be decreased by 0.9%, the annual energy output of the STP was increased by approximately 160 GWh. [PERSON] et al. [24] have developed a mathematical model with the lumped parameter method to verify the dynamic characteristics of an existing STP. Additionally, [PERSON] et al. [25] have proposed an STP for both electricity and freshwater production based on reverse osmosis technology, with a total flow rate of 240.02 kg/s of water being obtained. Moreover, [PERSON] et al. [26] have suggested a hybrid STP, decreasing the heat losses of the central receiver due to convection and radiation by 24.8% and 42.4%, respectively. Finally, [PERSON] et al. [15] have proposed the combination of a central receiver system with both a Brayton and an organic Rankine cycle (ORC), where if the CO\({}_{2}\) is chosen as the working fluid for the Brayton cycle, the total exergy efficiency increases while the cycle pressure ratio also increases. In the current study, a detailed dynamic investigation of a solar-driven Brayton cycle operating with supercritical carbon dioxide (sCO\({}_{2}\)) is presented. The solar thermal system is a central receiver system using two thermal storages. The simulation has been conducted using the Dymola software, where all the parts of the proposed configuration were programmed using the Modelica programming language in the Dymola environment. The simulation with this tool presents the possibility for an analysis with a high level of detail using an adjustable time step, as well as the application of advanced control techniques. Specifically, this study proposes a simple but accurate modeling based on analytical equations for calculating the performance of an STP, taking advantage of the Modelica language to solve the algebraic equation system without the need to provide a specific computational strategy. Different components have been developed, and they have been inserted in the Dymola environment by using the proper connections among them. The parameters of the problem have to do with the solar thermal system and the thermodynamic cycle, as well as with the followed control strategy.
## 1 Introduction
Moreover, to achieve an IoT-based system capable of supporting real-time data exchange for remote monitoring in precision agriculture, a variety of wireless communication standards and technologies for IoT applications, including the SigFox (0.868-0.869 GHz and 0.902-0.928 GHz), LoRaWAN (Long Range Wide Area Network) (0.867-0.869 GHz and 0.902-0.928 GHz), Narrowband-Internet of Things (NB-IoT) (B3, B8, and B20), ZigBee (0.867, 0.914 GHz), and Bluetooth (2.4-2.4835 GHz), have been proposed [29; 30; 31; 32; 33; 34]. In this paper, we present, for the first time, several models (Multi-slope, Weissberger, and COST-235) suitable for planning radio coverage in a corn field for smart agriculture applications. Radio wave propagation measurements for real smart agriculture scenarios in a corn field at two different frequencies (0.9 GHz and 2.4 GHz) using reference dipoles are presented. The dipole within the corn field was located at one-third of the wavelength (0.11 m and 0.04 m at 0.9 GHz and 2.4 GHz, respectively) and 0.5 m above ground to represent near-ground and above-ground propagation. In addition, we present path loss at 0.9 GHz and 2.4 GHz under the two transmitting and two receiving antenna heights at horizontal and vertical polarization based on the measurement data in the corn field. Finally, we compare the measured path loss to the different propagation models to find the model(s) that best represent the propagation characteristics of various communication scenarios of smart agriculture in a corn field.
## 4 Algorithm Characterization
### Algorithm Range Simulation  
This section provides a simulation analysis of the scope of the algorithm. We use the bit error rate of the light emission sequence, obtained experimentally from previous research, when it is read at the receiving end.  
The experimental parameters are shown in Table 2. In addition, the standard deviation is 5 pixels, and the focal length is 0.027 m. When the receiver lens is pointed at the center of the light array, the error rate at different positions of the receiver is as shown in Figure 5. The triangle in the figure represents the center point of the light source array. The horizontal coordinate indicates the distance between the receiver and the center of the light source array, and the vertical coordinate indicates the algorithm error rate. As can be seen from Figure 5, the bit error rate has a minor effect on the algorithm. When the receiver is within a 6 m radius of the center of the light source array, the probability of error detection in the algorithm is about 2%. At this time, the error probability of the algorithm is extremely low, meeting the requirements of normal room size authentication.  
Figure 4: The influence of a light source imaging deviation on the error rate of the delay addition algorithm.
## 2 The Ubiquitous Sensor Network Platform
### The USN-Gateway  
Figure 2 shows how the USN-Platform interconnects smart applications and sensor networks. USN-Gateways act as the interconnection points between IoT infrastructures and the Enabler. They provide two main functions: communication protocol adaptation (from the particular sensor protocols, such as MQTT, to HTTP, WebServices and/or SIP) and sensor data adaption (from the particular protocols to OGC SWE information models--SensorML and O&M (see Section 2.3 below)). By virtue of these two sets of conversions performed by the USN-Gateways, the USN-Enabler can be independent of the particular networking and data technologies used in the sensor networks.  
In this view, smart devices, which may be deployed in heterogeneous environments, connect directly or through gateways to a core NGN using a variety of access technologies (GPRS, UMTS, WiFi, _etc._). In many cases the devices can use their own communication capabilities to establish an IP connection with external USN-Gateways, or they can even have the USN-Gateway itself integrated in them. In our implementation a major scalability-enabling point is that once smart devices are connected to USN-Gateways, they are managed just like any other user equipment in the IMS (IP Multimedia Subsystem). The IMS is the control component of the NGN.  
In our USN-Platform smart devices can be managed in similar fashion to how Telco platforms regularly handle millions of user terminals today. In addition, they automatically benefit from the range of interesting features that NGNs offer, which were mentioned previously. Particularly notable and important are those related to security and authentication (functions not offered by the OGC standards). NGNs also offer provisioning, multi-network support, and extended and flexible session control. This provides an excellent basis for features such as load balancing, session continuity, and quality adaptation. All of these functions, together with a list of well-known enablers (for presence, location, instant messaging, _etc._) [20], make it possible to deliver high quality service and to provide a deployment platform in which final services can be offered in a broad variety of secure and reliable environments (operator centers, client location, smart devices, _etc._).  
The internal design of the USN-Platform has further interesting features that increase the advantages of this architectural proposal. For example, the platform is a transactional system; it is reliable against application disconnections (applies retrial policies for notifications); it supports store & forward of configuration commands; it is scalable, _etc._ Furthermore, the platform has been engineered in such a way that it is capable of running not only on an NGN but also in other environments, and it can be deployed over broad IP infrastructures. However, in these other environments the additional features provided by the NGN (mainly security-related) would have to be provided by other (external) components.  
Figure 2: Integration of Smart Devices into a Telco USN-Platform.
# A Novel Discriminating and Relative Global Spatial Image Representation with Applications in CBIR
## 4 Datasets and Performance Evaluation
### Performance on Corel-1.5K Image Dataset  
To further assess the effectiveness of the proposed method, experiments are conducted on Corel-1.5 image benchmark. The image retrieval performance of Corel-1.5 dataset is analyzed using the visual vocabulary of different sizes. The optimal performance is obtained for a vocabulary size of 400. Table 3 provides a comparison of the mean average precision for the top 20 retrievals with the state-of-the-art image retrieval approaches [8; 24; 26].  
It is evident from the table that the proposed RGSIR provides better retrieval performance compared to the state-of-the-art approaches with higher retrieval precision values than those of the existing research. Experimental results demonstrate that the proposed approach provides [18.9%,  
Figure 11: Result of image retrieval for the semantic class ”Dinosaurs”.  
Figure 12: Result of image retrieval for the semantic class “Flowers”.  
3.78%) better performance compared to the method without soft assignment, i.e., SQ \(+\) Spatiogram [24] and {8.75%, 2.77%}, than the probabilistic GMM \(+\) mSpatiogram [24] in terms of precision and recall, respectively.  
The proposed approach based on relative spatial feature extraction achieves 7.9% higher retrieval precision compared to the image retrieval based on visual words integration of SIFT and SURF [8]. Our proposed approach provides {10.25%, 2.05%} better precision and recall results compared to the late fusion based approach [26]. The experimental results demonstrate that our proposed approach significantly improves the retrieval performance compared to the state-of-the-art image retrieval techniques.
## 3 Methodology
### Time-Series Feature Representation
#### 3.4.2 Type-Wise Contrastive Loss  
Multi-scale contrastive loss was employed to force the encoder to learn feature representations at multiple scales [39]. At each scale, the TS2 Vec model jointly leverages both instance-wise and temporal contrastive losses to capture a contextual representation of time series. In the instance-wise loss, representations of other instances at timestamp \(t\) were taken as negative samples to capture fine-grained representations for general time-series tasks. For time-series classification tasks, this restriction was too strict for different instances with the same class types (categories). Thus, we attempted to utilize a supervised type of information for labeled samples to lift this restriction by taking representations with the same class type \(c\) and at timestamp \(t\) as positive samples. The type-wise contrastive loss indexed with (\(i\), \(t\)) can be formulated as follows:  
\[l_{type}^{(i,t)}=-\text{log}\frac{\exp\left(r_{i,t}\cdot r_{i,t}^{\prime} \right)}{\sum_{j=1}^{B}\left(\exp\left(r_{i,t}\cdot r_{j,t}^{\prime}\right)+1 _{c_{ij}\
eq c_{j}}\exp\left(r_{i,t}\cdot r_{j,t}\right)\right)}\]  
where \(i\) is the index of the input sample, \(B\) denotes the batch size, and \(r_{i,t}\) and \(r_{i,t}^{\prime}\) denote representations for the same timestamp \(t\) and from two augmentations of one sample.