esa-sceva/satcom-chunk-collection huggingface dataset:
# Wireless Sensor Network-Based Rockfall and Landslide Monitoring Systems: A Review
### Monitoring Systems for Geohazards  
Monitoring and early warning are commonly studied and applied strategies for the prevention of geohazards. The solutions available in the literature perform well in certain tasks but are lacking in others, and it is not always clear which fits a specific application better. The main reason behind this diversity resides in the different implementation possibilities of a sensor network and in its multidisciplinary nature. Considering the subset of landslide and rockfall monitoring systems, the list of variables involved is large. Different technologies have been studied and applied to this range of variables. In situ sensor networks are employed to gather information on the geological conditions of the monitored area.  
In general, a monitoring system for geohazard prevention can be schematized as follows:  
* A sensing layer, where sensors are used to gather data about a specific physical dimension relative to a geomorphological condition;
* A data exchange layer, where wired or wireless technologies are used to transport the information;
* A data storage layer, where gathered data are stored in a database for successive analysis;
* A data analysis layer, where data are analyzed to obtain early warnings, risk assessment, and generally useful information.  
In the past, wired data acquisition systems were used to store data for the different applications of structural monitoring [18; 19; 20; 21; 22]. Wiring and power constraints can, in some cases, increase the overall cost and complexity of the system, thus limiting the number and locations of sensor nodes. Installation and maintenance costs may be increased too. In WSNs, the sensor nodes are deployed without wiring; thus, this technological solution offers an advantage in dealing with all the aforementioned issues. These positive aspects can be successfully applied to geohazard monitoring systems [23], leading to new, more flexible solutions.  
This paper aims to give a comprehensive overview of the scientific literature that deals with rockfall and landslide monitoring with systems based on wireless sensor networks. Over the years, the technological advances in geohazard monitoring systems have moved towards low-power and more efficient implementations. The information that has been gathered in this review can be used for the development of new systems. To this extent, the characteristics and performances of the state-of-the-art systems are provided to overcome current research limitations and embrace future trends and challenges. Section 2 reports a brief description of the state of the art related to WSNs. In Section 3, several works dealing with the aforementioned geohazard monitoring systems are reviewed, together with some other works dealing with different technologies that have been used. Section 4 reports a discussion based on the reviewed research and future perspectives. Section 5 summarizes the work carried out in this paper.
## 2 Data and Methods  
### Study Area  
The SAM is mainly located in the northern part of the Xinjiang Uygur Autonomous Region of China (Figure 1). The SAM ranges from 224 to 4265 m above sea level (a.s.l.) and has a continental temperate climate that is strongly affected by the Siberian cold air masses and the westerlies in winter [36]. There was an increasing trend observed in relative humidity from the 1900s to the 1990s that has since switched to a decreasing trend [37]. The high- to low-elevation vegetation zones across the SAM consist of alpine tundra with snow patches and bare rocks, alpine wetlands and meadows, needle-leaved forests and scrubs, subalpine meadows, montane steppes, and desert steppes [38]. The main rivers in this region include the Irtysh and Ulungur. The Irtysh originates in the SAM, flows northwest into Kazakhstan, and then joins the Ob River near the city of Khanty-Mansiysk, western Siberia, Russia, before draining into the Arctic Ocean [33; 39]. The Ulungur is an inland river that originates in western Mongolia and flows south into Altay Prefecture of northern Xinjiang of China, where it then flows northwest and empties into Ulungur Lake [40].  
Pastoral livestock husbandry is prevalent in the Altai Mountains, with annual increases in the number of livestock significantly exceeding the environmental capacity [41; 42]. Grazing begins in late April and ends in early October following the timing of snow cover in the SAM. The annual economic report of Altay Prefecture in 2017 ([[http://www.yalt.gov.cn/sjalt/dataalt.html](http://www.yalt.gov.cn/sjalt/dataalt.html)]([http://www.yalt.gov.cn/sjalt/dataalt.html](http://www.yalt.gov.cn/sjalt/dataalt.html)), accessed on 17 November 2021) stated that the total cropland area was 3.84 million mu (0.63 million acres), with oil crops, food crops, and pasture being the main crop types. There was 3.16 million livestock in the region, with goats and sheep, cattle, and horses accounting for 74.0%, 19.6%, and 4.2% of the total, respectively. The regional population and croplands are concentrated mainly on the floodplains of the Irtysh and Ulungur rivers. The population increased by 4.2% between 2007 and 2016,  
Figure 1: Map of the study area, with the locations of the meteorological and hydrological stations used in this study, as well as the reservoirs and lakes, cropland, and mountain glaciers indicated. The inset map shows the extent of the Altai Mountains (light brown), the location of the study area (box), and the major rivers in the region.  
with a 2014 urbanization rate of 56.9% in the Chinese Altay Prefecture. The average water availability (including surface water and groundwater) and consumption during the period 2007-2016 were 111.9 \(\times\) 10\({}^{8}\) and 36.7 \(\times\) 10\({}^{8}\) m\({}^{3}\), respectively, but there were significant interannual fluctuations in water availability.
## 1 Introduction  
Transparent conductive oxides (TCOs) are commonly used in the development of solar cells [1, 2], light-emitting diodes, or sensing devices, among others, which normally include ITO (In\({}_{2}\)O\({}_{3}\)Sn), FTO (SnO\({}_{2}\):F), or AZO (ZnO:Al). For their application, thin films need to present low resistance and high transmittance. Among the three mentioned TCOs, ITO displays the best characteristics and is therefore the most widely used [3]. However, it presents some important disadvantages too, for example the high price and the toxicity of the material due to the presence of indium [4]. Despite the fact that FTO presents good characteristics, sometimes it cannot be used due to fluor diffusion between layers [5, 6]. However, zinc oxide thin films present good stability, non-toxicity, and are a low-cost material [7].  
TCO based doped ZnO films are also widely used as they are relatively cheap [8], can be textured for light trapping, and readily produced for large-scale coatings. They allow tailoring of the absorption in the UV region (transparency in the VIS region) and, when doped with other elements, present good electrical behaviour [9]. Furthermore, ZnO is resistant to oxygen and moisture, has very good optical transparency, and has a flexible host crystal lattice able to accept a variety of dopant substitutions.  
Some deposition methods have been reported using different techniques, including the sol-gel method [10], laser deposition [11], magnetron sputtering [12], spray pyrolysis, atomic layer deposition [13, 14], or chemical baths [15, 16, 17]. The atomic layer deposition is the most commonly used technique for the development of this type of film due to the high homogeneity and control of the grown film. Despite this, the technique has a low grow rate and requires very controlled atmospheres, which makes the industrial scaling difficult. Methods in which highest quality films are obtained are expensive and difficult to incorporate in less advanced industries; for this reason, it was considered convenient to study the optimization of a spray pyrolysis method using a manual aerograph, due to the simplicity of the process with similar optical and electrical results and the physics processes as an alternative to the ALD process.  
The films properties will change when exposed to spray pyrolysis, principally depending on the substrate temperature, atmosphere composition, carrier gas, droplet size, distance between the nozzle, the substrate, cation-anion ratio, and precursor solution concentration [18]. It gives to the technique a wide range of possibilities to improve and change the films' properties.
## 2 System Model
### Scenarios Based on 5G New Radio 3D Scenarios  
There are several 5G test scenarios [14; 15]. In this article, we have considered three modified scenarios that are described next and later evaluated using OFDM with mMIMO and RIS communications. For increasing frequency bands, a decrease in the range occurs due to higher power attenuation. We have chosen frequency bands adequate for each scenario. The lower frequency band (sub 6 GHz) with a larger range is adequate for the smart city scenario. The intermediate mmWave with a 28 GHz frequency carrier will be tested for the smart city with RIS and also for the stadium with a pitch scenario. The upper frequency of 100 GHz (THz) with a smaller range will be tested for the indoor factory scenario. While the adoption of BS antenna arrays and RIS panels with a large number of elements (\(N_{rx}/N_{rx}\) and \(N_{ris}\)) can result in substantial performance improvements in any of the bands, the longer wavelength in the 3.6 GHz band imposes a major physical constraint on the deployment of these large panels. Therefore, it is more feasible to harvest the potential gains associated with the adoption of large antenna arrays and RISs in the 28 GHz and 100 GHz bands, which can help reduce the performance gap relative to the 3.6 GHz case.  
The user mobility is selected based on the type of user and the scenario. For IoT devices in the smart city scenario, we have chosen static IoT devices, so the velocity is set as 0 km/h. For the other scenarios, pedestrians have a velocity of 3 km/h, unless they are in the stadium stands, where they are also static.
# Deep Class-Guided Hashing for Multi-Label Cross-Modal Retrieval
## 3 Methodology
### Optimization
(2) DCGH-_X-V_: uses only pairwise loss, \(\mathcal{L}_{pair}\), to train the model. Since proxy loss was not considered, \(\alpha\) and \(\beta\) were set to 1. (3) DCGH-_V_: variance constraint, \(\mathcal{L}_{var}\), was not used. The ablation experiment results are shown in Table 6. Comparing the results of DCGH-_P-V_ and DCGH-_X-V_ on three benchmark datasets reveals that DCGH-_P-V_, which only uses proxy loss, generally performs better on large datasets like NUS-WIDE and MS COCO than DCGH-_X-V_, which only uses pairwise loss. As the amount of data increases, the issue of intra-class dispersion caused by pairwise loss can significantly affect the effectiveness of the hash codes. Comparing the performance of DCGH-_V_ with DCGH-_P-V_ and DCGH-_X-V_ across the three datasets shows that DCGH-_V_, which combines proxy loss and pairwise loss to consider both intra-class aggregation and inter-class structural relationship preservation, significantly outperforms DCGH-_P-V_ and DCGH-_X-V_, which only use one type of loss each. This confirms the rationality of our combination approach. By comparing the results of DCGH and DCGH-_V_ on the three benchmark datasets, it is evident that introducing a variance constraint to prevent semantic bias leads to better results, confirming the effectiveness of the variance constraint. By comparing three variables, the effectiveness of each component of the DCGH method was verified. By combining proxy loss, pairwise loss, and variance constraints, DCGH is able to learn excellent hash codes. ### Sensitivity to Hyperparameters

We also investigated the sensitivity of the parameters \(\alpha\) and \(\beta\). We set their ranges to [0.001, 0.01, 0.05, 0.1, 0.5, 0.8, 1], and the results are reported in Figure 9. From Figure 9,d, it can be observed that the trend of alpha and beta on the MIRFLICKR-25K dataset is quite intuitive, with the best performance occurring at alpha and beta values of 0.05 and 0.8, respectively. Figure 9,c show that on large-scale datasets, as the value of alpha increases, meaning the constraint between positive samples becomes stronger, proxy loss can no longer dominate the training of the hashing network, leading to intra-class dispersion and a significant drop in mAP scores. Figure 9 indicates that due to the more relaxed constraint on negative samples, the increase in beta has a smaller impact on the hashing network. From Figure 9, we also find an interesting phenomenon that the value of beta has almost no effect on the mAP scores on the NUS-WIDE dataset, which we believe may be due to the overwhelming size of the NUS-WIDE dataset with only 21 categories, and the proportion of unrelated samples is too small. This also demonstrates the ingenuity of our approach to set separate weight parameters for positive and negative sample constraints in pairwise loss.
# Long-Range Wireless Radio Technologies: A Survey
## 2 Long-Range Wireless Technologies
### ISM Band-Based Technologies
#### 2.2.3 Ingenu  
Ingenu is another proprietary LPWAN technology. In contrast to other LPWAN, Ingenu proposes to use the 2.4 GHz ISM band, which is already used by other technologies (e.g., Wi-Fi), instead of the sub-GHz ISM bands. The advantage is that this band does not fall under heavy restrictions regarding duty cycle as the sub-GHz does. Ingenu is based on a proprietary physical technology known as Random Phase Multiple Access (RPMA), which is a variation of Code Division Multiple Access (CDMA). Ingenu is reported to have better performance than most of the other LPWAN technologies [25], especially in terms of range and data rate (78 kbps up-link and 19.5 kbps down-link). However, no scientific study is available to verify these claims.
## 4 Platform and Sensors Implementation and Evaluation
### Sensors
#### 4.2.1 Accelerometers
The same behavior is also obtained from these two sensors for the rest of windows in the test. Figure 4: Frequency domain analysis of the raw data obtained in the test. The results obtained in the test show that the use of a low cost accelerometer to obtained acceleration information in the frequency domain in this application domain seems feasible. As a result we have selected the ADXL345 sensor for installation in Sensor4 PRI.
# Improved Traffic Sign Detection and Recognition Algorithm for Intelligent Vehicles
## 3 Improved LeNet-5 CNN Model
### Improved LeNet-5 Network Model
#### 3.2.2 Improved LeNet-5 Network Model
After the offset correction, the Adam method can control each iterative learning rate within a certain range, thereby ensuring a smooth updating of the network parameters. The first moment of the gradient is as follows:  
\[m_{t}=\beta_{1}m_{t-1}+(1-\beta_{1})g_{t} \tag{8}\]  
The second moment of the gradient is as follows:  
\[v_{t}=\beta_{2}v_{t-1}+(1-\beta_{2})g_{t}^{2} \tag{9}\]  
where \(\beta_{1}\) and \(\beta_{2}\) are the attenuation factors, and \(g_{t}\) is the gradient value of the loss function at time \(t\). The first moment deviation estimate of the gradient is as follows:  
\[\hat{m}_{t}=\frac{m_{t}}{1-\beta_{1}^{t}} \tag{10}\]  
The second moment deviation estimate of the gradient is as follows:  
\[g(x,y,f,\theta)=\frac{1}{2\pi\sigma_{x}\sigma_{y}}\exp(-\frac{k_{1}^{2}}{2 \sigma_{x}^{2}}-\frac{k_{2}^{2}}{2\sigma_{y}^{2}})\exp\Bigl{(}i(f_{x}x+f_{y}y )\Bigr{)} \tag{1}\]0  
The gradient update formula of the Adam method is as follows:  
\[g(x,y,f,\theta)=\frac{1}{2\pi\sigma_{x}\sigma_{y}}\exp(-\frac{k_{1}^{2}}{2 \sigma_{x}^{2}}-\frac{k_{2}^{2}}{2\sigma_{y}^{2}})\exp\Bigl{(}i(f_{x}x+f_{y}y )\Bigr{)} \tag{1}\]1  
where \(\eta\) is the initial learning rate. The Adam method is computationally efficient and requires less memory space. Thus, this method is suitable for solving optimization problems with large-scale data and parameters. The Adam method can effectively solve the problems of learning rate disappearance, slow convergence and large fluctuation of loss function in the optimization process, thereby possessing a good convergence mechanism. (5) The dropout is added to the fully-connected layers. It temporarily discards half of the data flowing through the network by discarding some neurons. Before the new round of data iteration, the original fully connected model is restored, and then some neurons are randomly removed. The dropout can considerably reduce the amount of network computation, help weaken the joint adaptability between neuron nodes, enhance the generalization ability of the training model and play a regularization role to a certain extent to prevent over-fitting problems. Table 2 lists the parameter settings of the improved LeNet-5 network model. In this paper, the classical LeNet-5 network model is improved in many aspects and multiple levels. Considering the different interference conditions that may occur in the actual road scenes, the improved LeNet-5 network model integrates multiple advantages into one, thereby fostering strengths and avoiding weaknesses and complementing each other. The robustness and stability of the training network are considerably enhanced, and the overall convergence speed is improved, thereby further enhancing the performance levels of traffic sign classification and recognition.
## 4 Methods for Improved Reliability and Robustness
### Automatic Correction
#### 4.2.5 Ablation Analysis of MI Techniques  
We analyzed the reported performance accuracy of the different MI techniques and their ablated versions. By ablated version, we refer to the backbone architecture of each method without MI. Figure 2 summarizes the reported DSC and HD of the different methods. We observe a clear trend of improvement when using MI: there is an DSC increase, whereas the HD is reduced. Although the reported methods use different backbone architectures, configurations and datasets, which limit a direct comparison, there is a clear trend that suggests that MI techniques addressing robustness and reliability do have a positive impact in the performance of CMR segmentation methods.  
Figure 2: Average DSC (**left**) and HD (**right**) with (w/) the use of MI techniques and without (w/o) them.
# Service-Aware Clustering: An Energy-Efficient Model for the Internet-of-Things
### Contributions and Outline
#### 1.2.3 Centralized Routing  
We consider an approach where the clustering model is mapped into a centralized routing process where path finding and routing table computations are performed by the gateway, where sufficient computation resources are availed for centralized processing with the expectation of achieving an optimized routing configuration. The model uses information collected from the nodes by an energy-frugal distributed signaling protocol.
## 2 Materials and Methods
### Topology and Shape Optimization  
Topology optimization originated as a discipline rooted in deterministic functional analysis, initially employing tangible physical models. As mathematical understanding and techniques advanced, various methodologies were developed to tackle previously underdetermined problems. The introduction of virtual work significantly broadened the range of viable solutions for practical applications. Structural optimization is broadly classified into parametric and non-parametric approaches. Parametric optimization utilizes heuristic and metaheuristic methods to manipulate parameters within a defined domain, guided by well-approximated mathematical models that adhere to constraints and optimal  
Figure 1: Three-dimensional object simulation using photogrammetry.  
ity criteria. For example, sizing optimization directly links variables such as cross-section, material size, and hole placement to minimize stress.  
However, determining the optimal material distribution within a structural space presents challenges, as it cannot be straightforwardly represented by known quantities like masses, loads, or reactions. The difficulty lies in establishing a direct correlation between topology design and known constraints while formulating a universally applicable parametric objective. This challenge has spurred the development of non-parametric optimization methods, exemplified by topology optimization encompassing layout and shape optimization.  
Topology optimization, known for its conceptual clarity rooted in mathematical rigor, opens new avenues in design. It focuses on mathematically optimizing a predefined state space or higher-order set of criteria, driven solely by the imperative to fulfill the optimization process. The resultant designs theoretically offer the best possible solutions within defined constraints, representing an optimal solution bounded by prescribed parameters [73, 74, 75].  
This paper addresses the optimal topology of the structure, aiming to maximize the natural frequency of a specified mode while imposing a mass constraint to limit material usage. The optimization objective is to enhance the natural frequency of the selected mode, with the volume fraction of each node serving as the design variable. The topology optimization problem can be formulated as shown in Equation (3):  
\[\begin{array}{ll}\max&\omega_{i}\\ s.t.:&(\mathbf{K}-\omega^{2}\mathbf{M})\mathbf{U}=\mathbf{F}\\ &\int_{\Omega_{d}}pd\rho\leq v,\ \ \rho\in(0,1],\ \ \forall\rho\in\Omega_{d}\end{array} \tag{3}\]  
where \(\omega_{i}\) represents the eigen frequency of the designated mode, and \(\rho\) is the design variable of topology optimization. K is the global stiffness matrix, and M is the global mass matrix. Moreover, \(v\) is the volume reduction condition within the design domain \(\Omega_{d}\).  
In the context of the present study, topology optimization is employed to maximize the first eigen frequency, i.e., \(\omega\) 1.