esa-sceva/satcom-chunk-collection huggingface dataset:
# Co-Clustering under the Maximum Norm+
## 3 Intractability Results
### Constant Number of Rows  
The reduction in the proof of Theorem 1 outputs matrices with an unbounded number of rows and columns containing only three different values. We now show that also the \"dual restriction\" is NP-hard, that is the input matrix only has a constant number of rows (two), but contains an unbounded number of different values. Interestingly, this special case is closely related to a two-dimensional variant of geometric set covering.  
**Theorem 2**: Co-Clustering\({}_{\sf{oo}}\) _is NP-hard for \(k=m=2\) and unbounded alphabet size \(|\Sigma|\)._  
Proof.: We give a polynomial-time reduction from the NP-complete Box Cover problem [16]. Given a set \(P\subseteq\mathbb{Z}^{2}\) of \(n\) points in the plane and \(\ell\in\mathbb{N}\), Box Cover is the problem to decide whether there are \(\ell\) squares \(S_{1},\ldots,S_{\ell}\), each with side length two, covering \(P\), that is \(P\subseteq\bigcup_{1\leq s\leq\ell}S_{s}\).  
Let \(I=(P,\ell)\) be a Box Cover instance. We define the instance \(I^{\prime}:=(\mathcal{A},k,\ell^{\prime},c)\) as follows: The matrix \(\mathcal{A}\in\mathbb{Z}^{2\times n}\) has the points \(p_{1},\ldots,p_{n}\) in \(P\) as columns. Further, we set \(k:=2\), \(\ell^{\prime}:=\ell\), \(c:=2\). See Figure 3 for a small example.  
The correctness can be seen as follows: Assume that \(I\) is a yes-instance, that is there are \(\ell\) squares \(S_{1},\ldots,S_{\ell}\) covering all points in \(P\). We define \(J_{1}:=\{j\mid p_{j}\in P\cap S_{1}\}\) and \(J_{s}:=\{j\mid p_{j}\in P\cap S_{s}\setminus(\bigcup_{1\leq l<s}S_{l})\}\) for all \(2\leq s\leq\ell\). Note that \((\mathcal{I}:=\{\{1\},\{2\}\},\mathcal{J}:=\{J_{1},\ldots,J_{\ell}\})\) is a \((2,\ell)\)-co-clustering of \(\mathcal{A}\). Moreover, since all points with indices in \(J_{s}\) lie inside a square with side length two, it holds that each pair of entries in \(\mathcal{A}_{1s}\), as well as in \(\mathcal{A}_{2s}\) has a distance at most two, implying \(\operatorname{cost}_{\infty}(\mathcal{I},\mathcal{J})\leq 2\).  
Conversely, if \(I^{\prime}\) is a yes-instance, then let \((\{\{1\},\{2\}\},\mathcal{J})\) be the \((2,\ell)\)-co-clustering of a cost at most two. For any \(J_{s}\in\mathcal{J}\), it holds that all points corresponding to the columns in \(J_{s}\) have a pairwise distance at most two in both coordinates. Thus, there exists a square of side length two covering all of them.
# Hadoop vs. Spark: Impact on Performance of the Hammer Query Engine for Open Data Corpora
## 5 The _Hammer_ Prototype
### Architecture of the Hammer Prototype  
In this section, we describe the architecture of the _Hammer_ prototype. The prototype, implemented in Java, is composed by two main components, as shown in Figure 2: the _Indexer_ and the _Query Engine_. Two service components complete the architecture: the _Crawler_ and the _MongoDB_ NoSQL database management system.  
* The _Indexer_ gets meta-data concerning data sets, without downloading the instances, and builds an _Inverted Index_; at the same time, the catalog of the corpus is built. Both the catalog and the inverted index are stored as collections of JSON objects within a _MongoDB_ database.
* The _Query Engine_ is exploited by users to actually retrieve data sets. The query is processed on the basis of the inverted index and meta-data stored in _MongoDB_. For data sets matching the query or some neighbour query, their instances are actually retrieved and filtered, in order to produce the output results.  
* The _Crawler_ is a service component exploited both by the indexer and by the query engine to access the Open Data portal, in order to get either meta-data about published data sets or instances.
* _MongoDB_ NoSQL DBMS provides storage support. We decided to adopt it due to the flexibility provided by the fact that it stores JSON objects in heterogeneous collections in a native way; this way, the prototype can be easily evolved and the database can be easily adapted.  
Focusing on the query engine, the internal architecture is organized in blocks, as depicted in Figure 3. The query engine is organized as a pool of _Executors_: each executor is responsible for performing one or two of the steps that constitute the retrieval technique presented in Section 3.3. Executors are organized in a processing pipeline, as depicted in Figure 3: the output of one executor is the input for the next one. On the left-hand side, we depicted the meta-data catalog stored within the _MongoDB_ database; on the right-hand side, we depicted the inverted index, stored within the _MongoDB_ database as well. In particular, the figure shows which executor exploits the catalog and/or the inverted index. Finally, notice in the bottom right corner of Figure 3, the _Crawler_: it is exploited by the last executor to get instances of data sets selected by the _Step 4/5 Executor_.  
The development of the _Hammer_ prototype evolved step by step: in the very early version, the prototype was designed as a single process tool. Then, we identified _Step4/5 Executor_ as the critical executor as far as performances are concerned; consequently, we mainly modified the above-mentioned executor in order to exploit the Map-Reduce paradigm, by means of the _Hadoop_ framework; then, _Hadoop_ was replaced by the _Spark_ framework. Finally, the algorithms were re-implemented by exploiting high-level abstractions provided by _Spark_.  
Figure 2: General architecture of the _Hammer_ prototype.
## 2 Related Works
### Semi-Supervised Medical Image Segmentation Methods
#### 2.2.3 GAN-Based Methods  
Generative Adversarial Networks (GANs) are employed to create realistic augmentations of medical images, which are subsequently used to train segmentation models. This method benefits from the adversarial process, which compels the model to produce segmentations that are indistinguishable from real, annotated images. GAN-based methods have demonstrated promise in capturing complex patterns and improving generalization across different domains. GANs have become a crucial component of semi-supervised medical image segmentation, providing innovative methods to effectively leverage unlabeled data. The selected studies highlight a variety of GAN-based techniques that enhance segmentation through different mechanisms. [PERSON] et al. [23] focused on semantic segmentation using generative models, emphasizing the importance of SSL and robust cross-domain generalization. However, the complexity inherent in GAN architectures presents challenges in training and may require significant computational resources. [PERSON] et al. [24] shifted towards a GAN-based semi-supervised approach for medical image segmentation, potentially simplifying the model training process and reducing the dependence on extensive labeled datasets. Yet, the stability of GAN training remains a concern, given its sensitivity to hyperparameter settings and the risk of non-convergence. [PERSON] et al. [25] presented a GAN framework that incorporates a pyramid attention mechanism with transfer learning. This novel approach shows promise for segmenting images with complex structures, but the addition of attention mechanisms may introduce complexity, potentially hindering model efficiency. In essence, GAN-based methods are vital in medical image segmentation, addressing challenges such as feature attention, transfer learning, and domain generalization. They pave the way for future research, demonstrating the versatility and potential of GANs to enhance the precision and efficiency of SSL models within medical imaging. However, to fully harness the capabilities of GANs in this area, ongoing challenges like training complexity, stability, convergence, and adaptability must be carefully addressed.
# Survey for Soil Sensing with IOT and Traditional Systems
## 2 Tradicitonal Soil Sensing System
### Soil Moisture  
Soil moisture is pivotal for facilitating the uptake of vital nutrients by plants. Accurate monitoring of soil moisture is key for promoting sustainable agriculture [3, 47, 48]. In the realm of soil moisture detection via reflectance spectroscopy, the fluctuation in light reflectance is particularly evident in the VNIR region [22, 49], especially within the water absorption bands at 1450 and 1940 nm [22, 50, 51, 52]. The findings in [50] illustrate that changes in relative reflectance based on soil moisture are contingent on moisture levels. Under typical agricultural scenarios with lower soil moisture, as moisture content rises, reflectance decreases.  
However, the significance of detecting soil moisture extends beyond its own measure. It also critically impacts the detection of other soil attributes. This is because reflectance spectroscopy hinges on how incident light interacts with a material's surface, and moisture presence can alter the absorption of other properties [53]. The research presented in [53] centers on assessing the potential of NIRS for analyzing moist field soils, gauging the influence of soil moisture on the accuracy of NIRS predictions of soil attributes, and evaluating the reliability of a NIRS multivariate calibration method. Tests on both air-dried and moist soils show that NIR-PLSR holds strong predictive accuracy for several soil attributes, including total C, organic C, inorganic C, total N, and clay.  
The study [54] seeks to mitigate the moisture effect on spectra during the prediction of Soil Organic Carbon (SOC) content. It adopts the external parameter orthogonalization (EPO) technique to counteract the moisture influence on spectral calibration. Meanwhile, the research in [55] delves into SOC prediction, utilizing the normalized soil moisture index (NSPI) to determine the moisture content of samples, achieving an \(R^{2}\) value of 0.74, and categorizing samples based on their spectral moisture content.  
Furthermore, [56] evaluate the combined impact of salt and moisture on soil reflectance spectra. Their findings indicate that the concurrent variability of salt and moisture content complicates the modeling based on soil reflectance quantification, preventing accurate assessments of either property.
# Multi-Source Data-Driven Terrestrial Multi-Algorithm Fusion Path Planning Technology
## 4 Experimental Design
### Experimental Data and Environment  
The road network data used in this experiment is derived from the offline OpenStreetMap (OSM) dataset for the Wuhan metropolitan area, downloaded in December 2024. After preprocessing, the dataset retains key road categories--such as motorway, primary, and residential--as well as traffic light information. These data are structured into a graph format to support path computation. The experimental route spans from Wuhan Tianhe International Airport to Wuhan Railway Station, covering a variety of road types including expressways, urban arterial roads, secondary roads, and residential streets. This route exhibits a high degree of structural complexity and serves as a representative case for evaluating path planning performance. The planning process relies solely on static traffic information and does not incorporate real-time traffic updates. The reinforcement learning component is trained and evaluated within a simulated environment constructed based on the OSM-derived map. The state space consists of local map features, the agent's current position, and the target location; the action space includes directional movements and detour maneuvers.  
To emulate multi-source data fusion in a cost-effective and reproducible way, this study utilizes third-party APIs that provide synthetic representations of real-world sensor streams. These include the following:  
* **GPS-derived trajectory data**: Emulates real-time vehicle positioning and routing.
* **Traffic congestion APIs**: Simulate external sensing from road-side cameras or inductive coils by providing region-specific congestion scores and delay estimates.
* **Environmental status inputs**: Include information such as blocked roads or traffic incident alerts, analogous to what might be obtained from radar or IoT-based city infrastructure.  
These sources are integrated into the planning model through feature normalization and timestamp alignment, forming the input tensor for our fusion-based trajectory planning framework. While the physical sensor implementation is beyond our current experimental scope, our framework remains extensible to real-world deployment with minimal structural changes.
## 3 Obstacle Avoidance Scenario
### Obstacle Avoidance Techniques
#### 3.4.5 Vector Field Histogram Method  
Vector field histogram is a real-time obstacle avoidance method for mobile robots developed by [PERSON] and [PERSON] [41]. This method works using three steps for obstacle avoidance. In the first step, the robot generates a two-dimensional sensory histogram around its body or a limited angle and starts updating the histogram data at every stage. In the second step, the two-dimensional histogram data are converted into one-dimensional  
Figure 12: Usage of fuzzy logic control: (**a**) multiple sensor setup with (**b**) the structure of the fuzzy controller. (**c**) Fuzzy control rules for steering away from front obstacles [305].  
histogram data. Finally, it selects the lower polar dense area and moves the vehicle, calculating the direction. In Figure 13, the 2D and 1D histograms are presented.  
The VHF algorithm was improved to VFH+ [312] and VFH*[313], respectively. VFH+ reduces the original VFH parameter tuning, and the VFH* method verifies that a particular candidate direction guides the robot around an obstacle. Lidar is a suitable sensor for approving VFH methods, since it can take high-resolution multiple ranging data in two dimensions. For example, [PERSON] et al. [314] used VFH+ with lidar to avoid obstacles using a hexa-copter and [PERSON] et al. [315] used original VHF with lidar for obstacle avoidance of automated guided vehicles.
## 2 System Description  
### Acoustic-Electric Channel  
Figure 1 shows a block diagram of the full system. The acoustic-electric channel is composed of two commercial PZT-5 piezoelectric disc transducers (Hongsheng Acoustics Co., Baoding, Hebei, China) and a 304 stainless steel plate (dimensions \(300\) mm \(\times\) 300 mm \(\times\) 40 mm). The transducers are co-axially aligned on opposite sides of the steel plate and bonded to its largest faces using Araldite 2014 epoxy (Huntsman International LLC., the Woodlands, TX, USA). The transducers are both 2 mm thick, which gives them thickness-mode resonance at about 1 MHz. In addition, the diameters of the transducers are both 80 mm.  
The exact resonant frequencies of the transducers were measured using a 4294A impedance analyzer (Keysight Technologies Inc., Santa Rosa, CA, USA) before they were bonded to the steel plate, and the results are presented in Figure 2. The resonant frequencies of the transmitter (TX) transducer and the receiver (RX) transducer are 945 kHz and 946.25 kHz, respectively. At the resonant frequencies, the magnitude of the impedances of the transducers has the minimum values [22]. Figure 2 also shows that the quality factor (\(Q\)) values of the transducers are smaller than unit at the resonant frequencies. The low \(Q\) values make the system high-loss but reduce the resonant frequency consistency requirements on the TX and RX transducers [17].  
The TX transducer is driven by the cascaded combination of a continuous-wave (CW) signal source and a 100-W linear RF power amplifier (PA) from Xinpin Technologies LLC (Shenzhen, Guangdong, China). The RX transducer picks up the transmitted ultrasonic wave. An RF AC-DC converter then converts the received CW signal into a usable regulated DC power supply, which can directly drive a load electronic circuit.
# Precoding for RIS-Assisted Multi-User MIMO-DQSM Transmission Systems
## 2 System Model
### Reception  
The impinging signals at the RIS are defined as  
\[\mathbf{r}\triangleq\sqrt{1/\alpha}\mathbf{G}\mathbf{t}_{\mathbf{x}}, \tag{9}\]  
where \(\mathbf{t}_{\mathbf{x}}\in\mathbb{C}^{N_{l}\times 1}\) is the signal vector transmitted by the BS and \(\sqrt{1/\alpha}\) is an attenuation factor. These signals are reflected by the RIS without adding noise. Then, the received signal by the \(k\)-th user in the destination is given by  
\[\mathbf{y}_{k}=\sqrt{\gamma_{k}}\mathbf{H}_{k}\mathbf{\Theta}\mathbf{G} \mathbf{t}_{\mathbf{x}}+\mathbf{n}_{k}, \tag{10}\]  
where \(\mathbf{\Theta}\) is the phase shift matrix, defined as \(\mathbf{\Theta}=\text{diag}(e^{j\theta_{1}},e^{j\theta_{2}},\ldots,e^{j\theta_ {N_{k}}})\). Here, \(e^{j\theta_{i}}\) denotes the phase shift of the \(i\)-th reflecting element at the RIS and \(\mathbf{n}_{k}\in\mathbb{C}^{N_{l}\times 1}\) stands for the noise. The noise samples are assumed to be independent and identically distributed (i.i.d) with \(\mathcal{CN}(0,\sigma^{2})\). \(\sqrt{\gamma}_{k}\) is the signal-to-noise ratio (SNR) at the destination. Instead of applying the phase shifts vector at the RIS, in this work, the signals with optimal phases and amplitudes required at the RIS are evaluated and implemented in the BS. Since only precoding is used, the phases in the RIS can be considered arbitrary or fixed. In this paper, the RIS is considered as \"blind\" with \(\mathbf{\Theta}=\mathbf{I}\).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Input Bits, \(\mathbf{a}_{k}\)** & **QSM 1** & **QSM 2** & **Output Signal** \\ \(\mathbf{b}_{1}\)\(\mathbf{b}_{2}\) & \(\mathbf{x^{\prime}}_{1}\) & \(\mathbf{x^{\prime}}_{2}\) & **DQSM** \\ \hline
0000 0000 & \(1+j,0\) & \(1+j,0\) & \(1.5+1.5j,0\) \\
0000 0001 & \(1+j,0\) & \(-1+j,0\) & \(0.5+1.5j,0\) \\
0000 0010 & \(1+j,0\) & \(1-j,0\) & \(1.5+0.5j,0\) \\
0000 0011 & \(1+j,0\) & \(-1-j,0\) & \(0.5+0.5j
# Cryptography in Wireless Multimedia Sensor Networks: A Survey and Research Directions
## 5 Research Directions
Therefore, the study of the complexity of encryption algorithms is highly required, aiming at the evaluation of computational cost, code complexity, memory usage, key size, packets size, communication cost and power consumption. Other cryptography paradigms, such as hashing for authentication purposes [86], may also be applied in WMSN scenarios. As sensors' hardware gets more powerful at affordable prices, wireless multimedia sensor networks can execute more efficient compression and encryption algorithms. For heterogeneous networks, the choosing of the proper algorithms is of paramount importance. In general, we canexpect that encryption algorithms to be designed so that they can automatically detect the hardware resources of sensor nodes, allowing dynamical adaptation. Another promising research direction is the definition of security frameworks [73; 87], which may combine the cryptography of multimedia data with watermarking, authentication procedures and different security defense measures. Secure protocols for data transmission and key management are desired for many scenarios, and security mechanisms for routing have also been proposed [23; 88]. Moreover, some works have proposed secure protocols in the Transport [24] and MAC [25] layers. At last, integrated mechanisms for protection from different attacks, such as DoS [20], are also desired. The security issues in wireless multimedia sensor networks are indeed very challenging, and security frameworks can be valuable when implementing real-world monitoring applications. Efficient data compression will be related to the performance of cryptography, specially in resource-constrained scenarios. However, the way multimedia data will be processed and potentially combined is also relevant. Actually, data aggregation allows the combination of data from multiple sensors to generate high quality information, reducing redundancy and data correlation [89; 90]. The main idea is to reduce the amount of data to be transmitted and possibly compressed. For multimedia sensing, data aggregation can be leveraged to achieve efficient compression and encryption, respecting the particularities of the different media types. Compression techniques and aggregation algorithms for multimedia content are very important in WSN design in order to reduce the communication overhead, save processing resources and reduce energy consumption when decreasing the amount of transmitted data [8]. Therefore, as encryption costs may be too stringent for wireless multimedia sensor networks, the way multimedia data will be sensed and processed may be exploited to optimize cryptography. Although these are promising research areas in the field of secure wireless multimedia sensor networks, new challenges may still emerge [14], fostering investigation efforts in this area.
## 1 Introduction
However, hospital environments can be busy and tumultuous places, and their hallways can be crowded spaces filled with people moving around or sitting, chairs, carts, medical equipment, beds, etc. Therefore, a robotic walking assistant (RWA) must avoid collisions with humans or objects at all costs while maintaining physical contact with the patient that is assisted during walking. Naturally, the patient-robot co-navigation in these crowded environments can be a very challenging task. Reinforcement learning (RL) [18] is a powerful technique for training deep learning models, particularly in scenarios where the model must learn to navigate a complex environment with obstacles. The key advantage of RL is its ability to identify the optimal path to take in the presence of obstacles and to generalize in unseen scenarios that may arise in a real-world environment. The ability to generalize is crucial for ensuring that the trained model can effectively navigate and respond to new and dynamic obstacles, making it a preferred method for training deep learning models in real-world applications. This paper aims to utilize a multi-purpose mobile manipulator to assist patients while walking by safely guiding them through crowded hallways. The contribution of this paper is a proposed deep reinforcement learning-based framework that enables safe patient-robot co-navigation, including static and dynamic obstacle avoidance, by utilizing the speed of the patient's gait and the information of the environment. The proposed framework is evaluated in a simulated environment, and the extended framework is successfully deployed in Gazebo, which is a realistic simulation environment. The frameworks and the environment are based on an open-source robot operating system (ROS) [19]. The rest of the paper is organized into the following sections; Section 2 discusses the related work, Section 3 defined the problem, Section 4 the proposed system for patient-robot co-navigation, and Section 5 presents and discusses the results. Section 6 concludes the work and discusses future directions of research.